Identify Goal
Define Problem
Define Problem
Gather Data
Define Causes
Identify Options
Clarify Problem
Generate Ideas
Evaluate Options
Generate Ideas
Choose the Best Solution
Implement Solution
Select Solution
Take Action
MacLeod offers her own problem solving procedure, which echoes the above steps:
“1. Recognize the Problem: State what you see. Sometimes the problem is covert. 2. Identify: Get the facts — What exactly happened? What is the issue? 3. and 4. Explore and Connect: Dig deeper and encourage group members to relate their similar experiences. Now you're getting more into the feelings and background [of the situation], not just the facts. 5. Possible Solutions: Consider and brainstorm ideas for resolution. 6. Implement: Choose a solution and try it out — this could be role play and/or a discussion of how the solution would be put in place. 7. Evaluate: Revisit to see if the solution was successful or not.”
Many of these problem solving techniques can be used in concert with one another, or multiple can be appropriate for any given problem. It’s less about facilitating a perfect CPS session, and more about encouraging team members to continually think outside the box and push beyond personal boundaries that inhibit their innovative thinking. So, try out several methods, find those that resonate best with your team, and continue adopting new techniques and adapting your processes along the way.
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In project management and team collaboration, problem-solving is the process of identifying and resolving issues that arise during a project. It is a crucial skill that helps fix broken processes, improve performance, and identify opportunities. Problem-solving enables project managers and team leaders to overcome challenges and achieve success.
In this blog article, we will explore 15 problem-solving strategies that can revolutionize your approach and help you achieve success. From effective communication techniques to fostering collaboration, these strategies are designed to tackle the most common obstacles encountered in project management. Get ready to unlock the potential of your projects and teams with these tried-and-tested problem-solving strategies!
This problem-solving technique aims to uncover a problem's underlying cause by repeatedly asking the question, "Why?". The goal is to dig deep and identify the root cause rather than addressing surface-level symptoms. By asking "Why?" five times or more, depending on the complexity of the problem, you and your team members can gain valuable insights into the chain of events or processes that led to the issue. This method helps expose weaknesses, improve processes, and achieve project goals.
A SWOT analysis is a valuable tool in project management that helps identify and evaluate the internal pros and cons, weaknesses and strengths, and the external impacts that can threaten your project. By examining these factors, project managers can better understand the project's current state and potential risks.
SWOT analysis provides insights that help decision-making, resource allocation, and risk mitigation strategies. It allows project teams to counter threats, address weaknesses, and capitalize on strengths, ultimately enhancing project success.
A skilled facilitator can provide objective guidance, ensure everyone's participation, and create a safe space for open discussions. They can help the project team effectively identify and evaluate strengths, weaknesses, opportunities, and threats.
Additionally, a facilitator can assist in summarizing and documenting the analysis, ensuring clarity and alignment among team members. Bringing in a facilitator enhances quality and efficiency, leading to better project outcomes.
Root Cause Analysis is a systematic approach used to identify the underlying causes of problems or incidents. It involves investigating the factors contributing to an issue rather than just addressing the symptoms.
By understanding the root causes, organizations can develop effective solutions to prevent similar problems from recurring in the future. This analysis helps improve processes, enhance quality, and reduce risks, ultimately leading to better outcomes and customer satisfaction.
Also known as the 6 W's, this technique is used to gather information and comprehensively understand a situation. It involves asking and answering six key questions: Who, What, Where, When, Why, and How. Addressing these questions helps team members and leaders analyze and evaluate a problem or decision from various angles, ensuring a thorough examination of the topic at hand. This method is widely used in journalism, problem-solving, and decision-making processes.
Working backward is a problem-solving approach where you start with the desired outcome and then identify the steps needed to achieve it. This method allows you to break down complex problems into smaller, manageable tasks.
By starting with the end goal in mind, you can create a clear roadmap and prioritize actions accordingly. Working backward helps ensure that your efforts are focused and aligned with the desired outcome, leading to more efficient and effective problem-solving.
Trial and Error is a problem-solving approach that involves trying different solutions and learning from the outcomes. It is a standard method used to discover what works and what doesn't in various situations. By systematically testing different options, you can identify the most effective solution through a process of elimination.
Trial and Error allow team members flexibility and adaptability, as it encourages learning from mistakes and refining strategies based on feedback. This approach can be beneficial when dealing with complex or ambiguous problems that require experimentation.
Risk analysis and mitigation play a crucial role in project management. By identifying and planning for potential risks, teams can prevent problems. One effective way to facilitate this process is by utilizing project management software.
Project management software such as ActiveCollab provides a centralized platform where teams can document and track risks throughout the project lifecycle. This software often includes features such as risk registers , where risks can be identified, categorized, and assigned to team members for mitigation. Additionally, the software may offer risk assessment tools that help teams evaluate the impact and likelihood of each risk.
With ActiveCollab, teams can collaborate in real time, ensuring all members can access the latest risk information. This promotes transparency and allows immediate communication and decision-making regarding risk mitigation strategies.
Using project management software for risk analysis and mitigation, your team members can proactively address potential challenges, minimize project disruptions, and improve project success rates.
Conflict resolution techniques are essential for maintaining healthy relationships and fostering a positive work environment. Some effective methods include active listening, communication skills training, mediation, negotiation, and compromise. Conflicts can be understood and resolved more effectively by actively listening to all parties involved and encouraging open and honest communication.
Mediation allows a neutral third party to facilitate discussions, while negotiation and compromise help find mutually agreeable solutions. These techniques promote understanding, empathy, and collaboration, improving relationships and increasing productivity.
Scenario planning and forecasting are essential tools used by organizations to anticipate and prepare for future uncertainties. Scenario planning involves creating multiple plausible narratives or scenarios to explore possible futures, allowing decision-makers to identify risks and opportunities. Forecasting, on the other hand, uses historical data and statistical models to project future outcomes.
Combining these approaches allows businesses to develop robust strategies and make informed decisions in an ever-changing and unpredictable environment. This proactive approach helps organizations adapt, minimize risks, and seize opportunities, ensuring long-term success and resilience in an uncertain world.
Brainstorming and ideation sessions are great for generating creative ideas and solutions. You can bring together a diverse group of individuals while these sessions help them with collaboration and free thinking. Participants in brainstorming sessions are encouraged to share their thoughts and ideas without judgment, allowing for a wide range of possibilities to be explored.
The goal is to generate as many ideas as possible, with the understanding that quantity leads to quality. Through active listening and open-mindedness, participants can build upon each other's ideas and spark new insights. This collaborative process fosters innovation and can lead to breakthrough solutions to complex problems.
Design Thinking is a problem-solving approach that emphasizes empathy, creativity, and collaboration. It involves understanding the needs and experiences of users, generating a wide range of ideas, prototyping, testing solutions, and iterating based on feedback.
It encourages a human-centered and iterative mindset, which leads to exploring multiple possibilities before arriving at a final solution. Design Thinking enables teams to approach challenges with an open mind, fostering innovation and driving meaningful change. By putting people at the heart of the process, Design Thinking helps create solutions that meet their needs and aspirations.
Creating a feedback loop with team members leads to a culture of continuous improvement. Regularly soliciting positive and constructive feedback allows open communication, builds trust, and enhances teamwork. Encourage team members to share their thoughts, ideas, and concerns in a safe and non-judgmental environment.
Actively listen to their feedback, acknowledge their contributions, and provide actionable insights to help them grow professionally. Remember, a well-functioning feedback loop promotes collaboration, boosts morale, and ultimately leads to better outcomes for the entire team.
Agile principles are values and practices that enhance flexibility and responsiveness in problem-solving. These principles prioritize individuals and interactions, working solutions, customer collaboration, and responding to change.
Agile principles promote a more efficient problem-solving process by encouraging frequent communication and collaboration. Iterative development, continuous feedback, and adaptive planning are key components of agile problem-solving, allowing teams to adapt and respond to changing requirements quickly. Focusing on delivering value to the customer and embracing change enables organizations to address problems efficiently and effectively.
Effective problem-solving is crucial in project management as it ensures that issues are identified, analyzed, and resolved promptly and efficiently. By integrating problem-solving strategies with project management software like ActiveCollab, teams can enhance collaboration and streamline workflow.
Integrating problem-solving strategies with project management software allows for better communication and coordination among team members. It enables teams to track the progress of problem-solving activities, assign tasks, and monitor deadlines, ensuring everyone is on the same page. This integration also facilitates the sharing of information and knowledge, enabling teams to leverage their collective expertise and experience.
Moreover, project management software such as ActiveCollab provides a centralized platform where team members can document and access relevant information, making it easier to analyze problems and make informed decisions. It also allows the implementation of feedback loops, enabling continuous improvement and learning from past experiences.
In conclusion, integrating problem-solving strategies with ActiveCollab enhances teamwork, improves communication, and facilitates the efficient resolution of issues. This integration ultimately contributes to the successful execution of projects and achieving desired outcomes.
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In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.
Podcast transcript
Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.
Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].
Charles and Hugo, welcome to the podcast. Thank you for being here.
Hugo Sarrazin: Our pleasure.
Charles Conn: It’s terrific to be here.
Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?
Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”
You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”
I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.
I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.
Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.
Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.
Simon London: So this is a concise problem statement.
Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.
Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.
How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.
Hugo Sarrazin: Yeah.
Charles Conn: And in the wrong direction.
Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?
Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.
What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.
Simon London: What’s a good example of a logic tree on a sort of ratable problem?
Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.
If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.
When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.
Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.
Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.
People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.
Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?
Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.
Simon London: Not going to have a lot of depth to it.
Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.
Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.
Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.
Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.
Both: Yeah.
Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.
Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.
Simon London: Right. Right.
Hugo Sarrazin: So it’s the same thing in problem solving.
Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.
Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?
Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.
Simon London: Would you agree with that?
Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.
You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.
Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?
Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.
Simon London: Step six. You’ve done your analysis.
Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”
Simon London: But, again, these final steps are about motivating people to action, right?
Charles Conn: Yeah.
Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.
Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.
Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.
Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.
Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?
Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.
You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.
Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.
Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”
Hugo Sarrazin: Every step of the process.
Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?
Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.
Simon London: Problem definition, but out in the world.
Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.
Simon London: So, Charles, are these complements or are these alternatives?
Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.
Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?
Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.
The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.
Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.
Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.
Hugo Sarrazin: Absolutely.
Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.
Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.
Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.
Charles Conn: It was a pleasure to be here, Simon.
Hugo Sarrazin: It was a pleasure. Thank you.
Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.
Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.
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BMC Pediatrics volume 24 , Article number: 591 ( 2024 ) Cite this article
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Thalassemia is one of the most common genetic disorders. Patients with beta-thalassemia major confront serious clinical and psychosocial challenges in their all lives, which require coping strategies. It appears that psychological interventions are necessary to improve their coping skills. The aim of this study was to determine the effect of applying emotional intelligence components on coping strategies in adolescents with beta- thalassemia major.
This randomized clinical trial study involved 60 teenagers with beta- thalassemia major who were divided equally into intervention and control groups. The experimental group participated in 9 sessions of an emotional intelligence program consisting of 90 min, held both virtually and in person, two sessions per week. We investigated problem-focused and emotion-focused (including positive emotion-focused and negative emotion-focused) coping strategies of both groups of adolescents using the Billings and Moos questionnaire before the intervention, immediately after the intervention, and one month after the intervention. Data were analyzed using SPSS 21. Then, according to the research objectives, independent t-tests, Chi-square, Mann–Whitney, repeated measures Analysis of Variance (ANOVA) and Bonferroni test were used.
In experimental group, the mean score of problem-focused (problem-solving, cognitive evaluation) and positive emotion-focused (social support) coping increased from ( 14.2 ± 2.6) and (5.0 ± 0.5) before the intervention to ( 29.6 ± 3.1) and (10.9 ± 1.3) one month after the intervention, respectively ( P < 0.001). However, the mean score of emotional inhibition and somatic inhibition (negative emotion-focused) decreased from ( 13.8 ± 1.7 ) and ( 6.7 ± 1.5 ) before the intervention to ( 8.6 ± 2.0 ) and ( 3.8 ± 1.8 ) one month after the intervention, respectively ( P < 0.001). While the mean score of problem-focused and emotion-focused coping strategies before and one month after the intervention remained stable in the control group.
Adolescents with beta-thalassemia suffer from psychosocial disorders and they also cope maladaptive with their illness. Applying emotional intelligence has improved their coping strategies. Caregivers should be encouraged to assess coping skills in teenagers with beta-thalassemia major and use methods such as emotional intelligence to improve them. Therefore, it can help these adolescents to deal effectively with stress and complications of the disease.
IRCT20210521051356N1 (17/06/2021).
Peer Review reports
Thalassemia is a heritable disorder characterized by a defect in the production or non-production of the globin chain of hemoglobin [ 1 , 2 ]. This synthetic defect leads to severe anemia, requiring regular blood transfusions and iron chelation therapy [ 3 ]. Thalassemia is the most prevalent inherited hemoglobin disease reported in more than 60 countries [ 1 ]. This chronic disease has great incidence in the Middle East, Mediterranean, Indian subcontinent, and Southeast Asia [ 4 ]. Iran is a country with about 26,000 patients with thalassemia, and its frequency is between 2.5% and 15% in different regions of the country [ 5 ].
Patients with Beta-thalassemia and their families bear a significant psychosocial burden throughout their lives [ 6 ], which is especially important during childhood [ 4 ]. These children suffer from physical, social, emotional and school problems [ 7 ]. Physical problems such as chronic anemia, bone deformities, growth retardation, delayed physical maturation, and short height cause poor body image and lack of self-confidence [ 3 , 8 ]. Also, they face social problems such as separation from family, physical and facial deformities, fear of death, and limitations in social activities, schoolwork, and play which can lead to depression and anxiety in these children [ 9 ]. Children with thalassemia think they are different from their friends and have negative beliefs about their lives. Therefore, they feel guilty, highly nervous, and low self-respect [ 7 ]. On the other hand, adolescents with thalassemia not only have to cope with the transition from childhood to adulthood and all the challenges that come with it, but also have to adapt to thalassemia and its treatment [ 10 ]. Therefore, coping with their emotional problems requires adaptive coping skills [ 11 ].
Coping is considered a response to reduce the emotional, physical and psychological distress related to challenging life situations and daily bothers [ 12 ]. Lazarus and Folkman (1980) defined coping as the cognitive and behavioral efforts to overcome, tolerate, or decrease external and internal demands and conflicts between them [ 13 ]. Coping strategies can be divided into two types: problem-solving strategies involve actively reducing challenging conditions, whereas emotion-focused coping strategies aim to manage the emotional impact of stressful or potentially stressful situations [ 14 ]. Effective coping styles alleviate emotional distress, while inappropriate coping strategies exacerbate the negative effects of stress [ 15 ]. Results of different studies indicate that children suffering from long-lasting illnesses are less able to cope adaptive with the disease and its side effects [ 1 , 5 , 12 ]. In the treatment of chronic diseases, the patient`s ability to effectively manage the burden of long-lasting illness in daily life can play an impressive role in treatment outcomes [ 1 , 16 ]. According to the importance of coping skills and the use of maladaptive coping strategies in dealing with stress in beta-thalassemia patients, more attention should be paid to improving coping strategies.
Bar-On & Parker (2000) describe coping strategies and adaptability as important components of emotional intelligence [ 17 ]. Salovey and Mayer (1990) introduced emotional intelligence as the ability to observe feelings and emotions of individuals and others, to differentiate between them, and to use this data to guide one`s beliefs and activities [ 18 ]. According to the Bar-On model, emotional intelligence includes emotionally and socially-related skills and abilities that specify how to understand and represent oneself, realize other people and communicate with them, and cope with everyday demands [ 19 ]. Bar-On identified 5 components of emotional intelligence that comprise 15 subscale, including: Intrapersonal (comprising self-regard, emotional self-awareness, assertiveness, independence, and self-actualization); Interpersonal (comprising empathy, social responsibility, and interpersonal relationship); Stress management (comprising stress tolerance and impulse control); Adaptability (comprising reality-testing, flexibility, and problem-solving); and General mood (comprising optimism and happiness) [ 19 ]. The results of some studies found that there was a significant correlation between emotional intelligence and coping strategies [ 14 , 20 , 21 ]. However, other studies indicate that there was not a meaningful relation between emotional intelligence and coping skills [ 17 , 22 ]. According to the importance of coping with complications of beta-thalassemia major, specific conditions of these patients and also the limitations of studies in this field in Iran, the purpose of the study was to examine the effect of applying emotional intelligence components on coping strategies in adolescents with beta-thalassemia major.
This was a randomized clinical trial study conducted to investigate the effects of applying emotional intelligence components on problem-focused and emotion-focused (including positive emotion-focused and negative emotion-focused) coping strategies in adolescents suffering from beta-thalassemia major referred to Thalassemia and Hemophilia Clinic Center (Sarvar) from July 6th to October 30th 2021 (with the trial registration number IRCT20210521051356N1 in 17/06/2021). This study examined the effects of applying emotional intelligence components on two types of coping skills comprising problem-focused strategies (problem-solving and cognitive evaluation) and emotion-focused strategies, including positive emotion-focused (social support) and negative emotion-focused (emotional inhibition and somatic inhibition). This article is written based on the TREND statement checklist.
The inclusion criteria were 1) Having the informed consent to participate in the study, 2) Willingness to participate in the study, 3) having a medical record diagnosed with beta-thalassemia major, 4) Aged between 12 and 18 years, 5) Having the ability to read and write, 6) do not suffer from mental illness based on the medical records, 7) do not pass similar courses of emotional intelligence, 8) Lack of complementary therapies (such as acupuncture, yoga).
The exclusion criteria were 1) absence in 2 sessions or more of educational intervention 2) unwillingness to continue cooperation with the researcher during the study, 3) Failure to participate in the post-test 4) occurring stressful events for the patient or his/her family.
The data were gathered through demographic information form and Billings and Moos coping strategies questionnaire.
Demographic data included 13 questions of gender, age, educational status, parental economic status, insurance, underlying disease, marital status, and duration of illness, hemoglobin level, ferritin level, history of splenectomy, iron chelation drug and occupation. Billings and Moos (1981), designed a questionnaire consisting of 19 Yes/No questions to assess coping strategies in difficult situations. Its modified version was published by them in 1984 and contained 32 items and evaluated two domains of problem-focused coping (comprising two subscales of problem-solving and cognitive evaluation) and emotion-focused coping (comprising three subscales of social support, emotional inhibition and somatic inhibition) [ 21 , 23 ]. All these items were scored through a 4-point Likert scale from 0(never used) to 3(always used). The minimum and maximum scores are 0 to 96, respectively; the higher scores show a greater use of all the above-mentioned coping strategies. The retest reliability coefficient was reported 0.79 and for problem-solving, cognitive evaluation, social support, emotional inhibition and somatic inhibition were 0.90, 0.68, 0.90, 0.65 and 0.90, respectively. The internal consistency reliability of the Persian version for the whole questionnaire was considered to be 0.41 to 0.66 [ 24 ]. In this study, Cronbach’s alpha coefficients for problem-solving, cognitive evaluation, social support, emotional inhibition and somatic inhibition subscales were 0.76, 0.74, 0.78, 0.72 and 0.84, respectively. Moreover, the validity of this tool was determined by the content validity method. This questionnaire was approved by 10 experts and specialists in the field of the research topic. Back-translation method was used to translate the questionnaire by two independent translators.
The study samples were recruited through convenience sampling based on the inclusion criteria in Thalassemia and Hemophilia Clinic Center (Sarvar). After explaining the main aim and methodology of the research to the teenagers and their parents, the written consents were obtained from adolescents and their fathers by researcher in Sarvar Clinic, as well as a form to collect demographic information. If the father was absent for any reason and the mother was with adolescent, the written consent was obtained from the mother. Then, potential participants were divided into two groups of intervention and control based on the days of referral (Even and odd days). Lottery was used to determine which day (odd or even) belonged to which group (intervention or control). Then, the intervention group divided into groups of 3–5 people based on lottery. This lottery was conducted by the researcher in the clinic. A number was assigned to each sample of the intervention group. She wrote the numbers in small papers and folded the paper. Then she poured it into a container and blended it. Then, in the presence of other authors, 3 to 5 papers were selected and placed in a group.
The intervention group members took part in training sessions about emotional intelligence components based on Bar-on model. The educational concepts of the classes were designed based on 5 components in Bar-on model as shown in Table 1 [ 19 , 25 ]. Emotional intelligence training was provided in 9 sessions consisting of 90 min, two sessions per week; the intervention was to be delivered over one month period. Classes were held virtually and in person in groups of 3–5 individuals using lectures, discussion, questions and answers by researcher and clinical psychologist. Virtual meetings were held in WhatsApp by voice and video call. The face-to-face sessions were delivered in the room of Sarvar Clinic. The training content of the classes was the same for all experimental groups (Table 1 ). The participants of the intervention group were provided with an educational booklet about the emotional intelligence and asked to practice at home daily and discuss their opinions at the beginning of the next session. For ethical reasons the control group members were provided with an educational booklet at the completion of the study. Moreover, the standard of care (including the Periodic medical visit, blood transfusion, iron chelation therapy, giving educational pamphlets, self-care training) was carried out. The intervention groups were asked not to talk with control group members about implementation due to avoid exchanging data between them, all participants including experimental and control groups were asked to complete the Billings and Moos questionnaire before the intervention, immediately after the intervention, and one month after the intervention. The participants were asked to think about the stressful events that happened to them in the last month and answer the questionnaire based on how they responded to the stressful events. A face-to-face self-report interview was used to collect information.
To calculate the sample size, we using G-POWER software and t test with the power of 80% and the error probability ratio of 5%, 33 individuals were assigned into each group [ 26 ]. Then by considering attrition rate, 2 more individuals were added into each group and finally each group comprised 35 people. We performed a statistical power analysis using independent t test. The power was 0.20 with considering 35 individuals in each group.
The collected data analysis was performed using The SPSS software (version 21). For examining and summarizing demographic data of subjects, descriptive statistics, such as frequency, percentage, mean and standard deviation (SD) were used. We tested the normality of quantitative variables by using Kolmogorov–Smirnov and Shapiro Wilk tests. To examine the homogeneity of the two groups in terms of intervening and background variables, chi-square (qualitative variables), independent t (quantitative variables with normal distribution) and Mann–Whitney (quantitative variables without normal distribution or rank) was used. To achieve the objectives of the research, repeated measures Analysis of Variance (ANOVA) and Bonferroni were used. Statistical significance was considered at p < 0.05 and reliability 95%. The statistical analyst was blinded to minimize the bias.
The per protocol analysis used for this clinical trial. During the study, 31 of the 35 teenagers in the intervention group completed the training courses. 1 of the 31 who were in the experimental group and 5 of the 35 adolescents in the control group were not able to complete the second and third evaluation. Finally, the study was performed on 60 individuals ( 30 in the intervention group and 30 in the control group) and the results were calculated without missing samples (Fig. 1 ; Diagram of the participants in the study).
Diagram of the participants in the study
The mean age of the participants was 15.6 ± 1.8 and 15.5 ± 2.3 years in the intervention and control groups, respectively. 33.3% (n = 10) were female and 66.7% (n = 20) were male in the intervention group and 33.3% (n = 10) were female and 66.7% (n = 20) were male in the control group. Considering the duration of disease in the experimental group was 15.0 ± 2.0 years and in the control group was 15.1 ± 2.2 years (Table 3 ).
First, the normality of quantitative variables was tested by using Kolmogorov–Smirnov and Shapiro Wilk tests (Table 2 ). Independent t-test (for normal quantitative variables), Mann–Whitney test (for non-normal quantitative and rank variables) were used to compare two groups. Nominal variables were also compared in two groups using the chi-square test. This statistical analysis showed there were no meaningful differences between the demographic variables of intervention and control groups (Table 3 ). Statistical significance was considered at p < 0.05.
A repeated-measures ANOVA was performed to evaluate the effect of applying emotional intelligence components on coping strategies. The means and standard deviations for coping strategies are presented in Table 6 . Mauchly’s test indicated that the assumption of sphericity had been violated, [Mauchly’s W = 0.676] and [ P < 0.001], and therefore degrees of freedom were corrected using Huynh–Feldt as presented in Table 4 .
The effect of time on total score of coping strategies was significant at the [0.05] level, F-ratio = [777.906], p = [< 0.001], partial Eta Squared = [0.86] and degree of freedom = [1.557]. The effect of group on total score of coping strategies was significant at the [0.05] level, F-ratio = [9022.576], p = [< 0.001], partial Eta Squared = [0.98] and degree of freedom = [1]. The results of weighted repeated measures analysis of variance (ANOVA) of coping strategies based on the effect of time and group are presented in Table 5 .
Post-hoc pairwise comparisons with a [Bonferroni] adjustment indicated that there was no significant difference between the total scores of coping strategies in the control [34.73 ± 5.420] and experiment [34.90 ± 6.359] groups before the intervention ( p = [0.169]). However, there was a significant difference between the total scores of coping strategies before the intervention [the initial assessment] and immediately after the intervention [the follow-up assessment] in the control [34.80 ± 5.359] and intervention [39.07 ± 6.297] groups, ( P < 0.001). Similarly, there was a significant difference between the total scores of coping strategies immediately after the intervention [the first] and one month after the intervention [second follow up assessments] in the control [34.83 ± 5.331] and intervention groups [41.20 ± 6.440], ( P < 0.001). Moreover, the total scores of coping strategies were significantly higher at the second follow-up assessment [one month after the intervention] than the first assessment [immediately after the intervention], ( P < 0.001). The results of Bonferroni test are shown in the Table 6 .
The effect of time on problem-focused strategies was significant at the [0.05] level, F-ratio = [7951.162], p = [< 0.001], partial Eta Squared = [0.98] and degree of freedom = [1.455]. The effect of group on problem-focused strategies was significant at the [0.05] level, F-ratio = [1407.417], p = [< 0.001], partial Eta Squared = [0.90] and degree of freedom = [1] (Table 5 ).
Post-hoc pairwise comparisons with a [Bonferroni] adjustment indicated that there was no significant difference between the scores of problem-focused strategies (problem-solving and cognitive evaluation) in the control [14.200 ± 2.551] and experiment [14.233 ± 2.661] groups before the intervention ( p = [0.913]). However, there was a significant difference between the scores of problem-focused strategies before the intervention [the initial assessment] and immediately after the intervention [the follow-up assessment] in the control [14.266 ± 2.476] and intervention [24.333 ± 2.941] groups, ( P < 0.001). Similarly, there was a significant difference between the scores of problem-focused strategies immediately after the intervention [the first] and one month after the intervention [second follow up assessments] in the control [14.400 ± 2.443] and intervention groups [29.633 ± 3.189], ( P < 0.001). Moreover, the scores of problem-focused strategies were significantly higher at the second follow-up assessment [one month after the intervention] than the first assessment [immediately after the intervention], ( P < 0.001) in the intervention group (Table 6 ).
The effect of time on total emotion-focused strategies was significant at the [0.05] level, F-ratio = [846.535], p = [< 0.001], partial Eta Squared = [0.94] and degree of freedom = [1.380]. The effect of group on total emotion-focused strategies was significant at the [0.05] level, F-ratio = [901.775], p = [< 0.001], partial Eta Squared = [0.94] and degree of freedom = [1] (Table 5 ).
Post-hoc pairwise comparisons with a [Bonferroni] adjustment indicated that there was no significant difference between the scores of emotion-focused strategies in the control [20.533 ± 4.023] and experiment [20.666 ± 5.261] groups before the intervention ( p = [0.913]). However, there was a significant difference between the scores of emotion-focused strategies before the intervention [the initial assessment] and immediately after the intervention [the follow-up assessment] in the control [20.533 ± 4.049] and intervention [14.733 ± 5.258] groups, ( P < 0.001). Similarly, there was a significant difference between the scores of emotion-focused strategies immediately after the intervention [the first] and one month after the intervention [second follow up assessments] in the control [20.433 ± 4.091] and intervention groups [11.5667 ± 5.230], ( P < 0.001). Moreover, the scores of emotion-focused strategies (emotional inhibition and somatic inhibition) were significantly lower at the second follow-up assessment [one month after the intervention] than the first assessment [immediately after the intervention] in the intervention group, ( P < 0.001). However, the score of social support was significantly higher at the second follow-up assessment [one month after the intervention] than the first assessment [immediately after the intervention] in the intervention group, ( P < 0.001) (Table 6 ).
Studies have indicated that patients with beta-thalassemia major suffer from psychosocial disorders. For instance, the study of Messina et al. (2008) revealed that thalassemic patients are likely suffer from psychiatric problems and impaired psychosocial functioning such as somatization, depression and obsessive–compulsive traits and also the most used coping skills was escape-avoidance [ 27 ]. Moreover, Adib-Hajbaghery et al. [ 3 ] reported that a majority of beta-thalassemia patients suffer from mild to severe depression, anxiety, and stress [ 3 ]. According to the psychosocial problems, it could be helpful to identify the strategies that can improve the coping skills of the patients. Therefore, in the present study, the effect of applying emotional intelligence components on coping strategies in adolescents with beta-thalassemia major was investigated.
Based on the findings of this study, Bonferroni test indicated no statistically differences in the mean scores of problem-focused and emotion-focused coping strategies in both intervention and control groups in the pre-intervention phase. Hence, it can be assumed the used coping strategies in the terms of types and amounts were same in the two groups before the intervention.
The present study showed that there are positive effects of applying components of emotional intelligence on problem-focused coping strategies and the mean scores of problem-solving and cognitive evaluations are significantly increased in adolescents with beta-thalassemia major. Simply put, patients in the experimental group used more problem-focused coping strategies after the intervention, in comparison to the control group.
In line with the present study, Kovaþeviü et al., (2018) conducted a study on patients with Schizophrenia, showed that there is a significant positive relation between emotional intelligence and problem-focused coping strategies [ 17 ]. Nogaj(2020), also found that emotional intelligence correlates positively significant with coping strategies among music school students in the context of visual art and general education students [ 28 ]. Additionally, the study of Moradi et al., [ 21 ] demonstrated that emotional intelligence had positive relationship with problem-solving and cognitive evaluation in university students [ 21 ]. Moreover, Noorbakhsh et al., (2010) revealed that emotional intelligence was positively associated with problem-focused coping strategies [ 14 ]. Furthermore, the study of Boyer et al., (2017) on depressed patients and their caregivers indicated their caregivers, who get higher score of emotional intelligence than patients, used more problem-focused coping strategies [ 29 ]. Besides, Delhom et al., [ 30 ] in the study on investigation the associations among emotional intelligence, coping and depressed mood, concluded that elderly people with high emotional intelligence are more likely to use problem-centered strategies and achieve better psychological adjustment [ 30 ]. These findings are consistent with the results of the present study, which indicates applying emotional intelligence can improve problem-focused coping strategies.
This study indicated applying components of emotional intelligence reduced the mean score of emotional and somatic inhibition and promote the mean score of social support. In other words, patients in the intervention group used more social support coping strategy and less emotional and somatic inhibition coping strategies after the intervention, in comparison the control group.
The result of Noorbakhsh et al., `study (2010) that aimed to determine the relationship between emotional intelligence and coping styles with stress, revealed that emotional intelligence was positively related with positive emotion-focused coping, and negatively associated with negative emotion-focused coping, which is in agreement with the present study [ 14 ]. Moradi et al., [ 21 ] also confirmed that emotional intelligence has positive correlation with social support and negative relationship with physical control coping strategies [ 21 ]. Moreover, in the study of Enns et al., (2018) participants with higher emotional intelligence showed lower perceived stress, used more of adaptive coping skills (included problem-solving and social support) and used less of maladaptive coping strategies [ 31 ]. Furthermore, the study of Hajisabbagh et al., (2017) on patients with epilepsy, confirmed that patients with lower emotional intelligence used more emotion-centered strategies and improving the emotional intelligence of these patients can help them to use more adaptive coping skills [ 20 ]. Additionally, Sarabia et al., (2017) found that coping styles were increased significantly by emotional intelligence workshop and emotional intelligence training can promote adaptive coping strategies [ 32 ]. These findings are in agreement with our study demonstrated that applying emotional intelligence components improve the coping strategies of patients and help them to cope better with stressful situations.
Contrary to our findings, Kovaþeviü et al., (2018) reported that emotion-oriented strategies had no meaningful relationship with emotional intelligence and its subscales [ 17 ]. It can be attributed to the difference in the research community; they conducted research on patients with schizophrenia between the ages of 18 and 55 and these patients have endured many complications of the disease and its treatments over the years, so it is not easy to change coping strategies in these patients and changing coping skills requires long-term intervention [ 17 ]. The study of Manicacci et al., (2019) on the relationship between emotional intelligence and coping strategies in Mothers of Autistic Children also indicated no significant correlation between emotional intelligence and coping strategies. It can be attributed to the difference in the research community and data collection method; they conducted the study on mothers of autistic children aged 23–61 years and outcomes show what they were thinking and feeling when they answered. Besides that, data collection method was anonymous and online, therefore researcher cannot confirm the children’s diagnoses indicated by the mothers, nor the degree of autism severity [ 22 ].
Moreover, the results reveal that the effect of time and group on all coping strategies is significant ( P < 0.001 ). Considering that the two groups were homogenous in terms of all the demographic characteristics, thus, the main effect of time and group on the average of coping strategies in adolescents with beta-thalassemia major in the two groups can be attributed to the effect of the research intervention including the applying of emotional intelligence components.
In conclusion, emotional intelligence training can improve coping strategies in adolescents with beta-thalassemia major. Emotionally intelligent people think differently about the stressful situation and they see it as an opportunity instead of a threat, they have better ability and more skills to control their emotions to cope with work-related problems and daily life challenges [ 33 ].
The present study has some limitations, including the small sample size, which limits the generalizability of the results. Moreover, because of the coronavirus pandemic, the most training sessions were held virtually. Moreover, our results show what the teenagers were thinking and feeling when they answered the questionnaires.
Overall, the findings of the present study demonstrated that applying components of emotional intelligence can promote problem-focused (problem-solving and cognitive evaluation) and positive emotion-focused (comprising social support) coping strategies and reduce negative emotion-focused (comprising emotional inhibition and somatic inhibition) coping strategies. In other words, applying emotional intelligence enabled adolescents with beta-thalassemia major to use adaptive coping skills and deal better with complications of the disease.
Health care providers are often the first point of contact with children and adolescents, in meeting with adolescents with chronic illness and their families, they can identify and evaluate coping strategies used and then apply methods to improve them. Promoting positive coping can play an important role in decreasing stress, ameliorating therapeutic outcomes and the quality of life. As a result, we strongly recommend that health professionals consider educating and applying emotional intelligence as part of psychological and non-pharmacological treatments in health centers.
The datasets generated and/or analyzed during the current study are not publicly available due to individuals’ privacy but are available from the corresponding author on reasonable request.
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We would like to express our gratitude for the support of the nurses of Thalassemia and Hemophilia Clinic Center (Sarvar) and the adolescents who participated in our study.
This study is the result of a master thesis in pediatric nursing with the code of grant 991978. The work was supported by the Research Administration of Mashhad University of Medical Sciences.
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MSc Student, Department of Pediatric Nursing, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran
Bahareh Ahmadian
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Monir Ramezani
Associate Professor, Department of Pediatric Nursing, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran
Associate Professor, Specialist in Pediatric Hematology and Oncology, Department of Pediatrics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
Zahra Badiei
Instructor, MSc, Department of Mental Health, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran
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BA, MR, ZB and SV made contributions to conception and design of the study, acquisition of data, analysis and interpretation of data. They all drafted the manuscript or revised it critically for important intellectual content. All authors of the manuscript have read and approved the final version of the manuscript.
Correspondence to Monir Ramezani .
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This study was awarded ethical approval by Mashhad University of Medical Sciences (with the code of ethics IR.MUMS.NURSE.REC.1400.017) on May 31, 2021. All methods were performed in accordance with the Declaration of Helsinki. The written informed consent form from all adolescents and their parents were obtained by researcher.
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Ahmadian, B., Ramezani, M., Badiei, Z. et al. The effect of applying emotional intelligence components on coping strategies in adolescents with beta-thalassemia major: a randomized clinical trial. BMC Pediatr 24 , 591 (2024). https://doi.org/10.1186/s12887-024-05057-7
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DOI : https://doi.org/10.1186/s12887-024-05057-7
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The blended synchronous classroom fosters equitable and balanced educational development; however, challenges persist, including low motivation and suboptimal effects on the deep learning of remote students. This study employs a student-centered learning approach and incorporates the process-oriented guided inquiry learning (POGIL) strategy into the blended synchronous science classroom. In this study, 182 fourth-grade students from four primary classes in south China were allocated using a quasi-experimental approach; among these four classes, one class from the urban area (proximal end) and one from the remote area (remote end) were paired, forming two matched classes, which constituted experimental group and control group. The experimental group took POGIL instruction, while the control group took traditional synchronous classroom instruction. The results indicated that students in classes using the POGIL intervention scored significantly higher than those in non-POGIL classes and that the POGIL instructional intervention not only facilitated the learning of science knowledge of the remote students but also promoted proximal students’ learning of science knowledge. In terms of deep learning, remote students in the POGIL class demonstrated significantly higher abilities in problem-solving, collaboration, communication, autonomous learning, self-efficacy, and perseverance in learning. Students expressed satisfaction with this instructional strategy. This paper discusses the effectiveness of applying the POGIL instructional strategy in teaching and technology support in the blended synchronous science classroom.
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Inquiry-based chemistry education in a high-context culture: a qatari case study.
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This work was supported by a project funded by the research project of the construction of the national collaborative innovation experimental base for teacher development at Central China Normal University — “Design and development of adaptive teacher training resources” (Grant Number CCNUTEIII 2021-04). This work was conducted as part of a Research Project Supported by the Fundamental Research Funds for the Central Universities (No. 2024CXZZ042). This work was conducted as part of Research on multiple design of primary school science synchronous classroom work under the vision of “double reduction” in the 14th Five-Year Plan of Basic Education Science in Liuzhou City in 2023 (Project No. 2023-A07).
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Qianqian Gao, Mingwen Tong, Jia Sun, Chao Zhang, Yuxin Huang & Si Zhang
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Qianqian Gao: data collection and analysis, writing—original draft. Mingwen Tong: writing—reviewing and editing. Jia Sun: writing—reviewing and editing. Chao Zhang: methodology, writing—reviewing and editing. Yuxin Huang: conceptualization, writing—reviewing and editing. Si Zhang: writing—reviewing and editing.
Correspondence to Mingwen Tong .
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Gao, Q., Tong, M., Sun, J. et al. A Study of Process-Oriented Guided Inquiry Learning (POGIL) in the Blended Synchronous Science Classroom. J Sci Educ Technol (2024). https://doi.org/10.1007/s10956-024-10155-3
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DOI : https://doi.org/10.1007/s10956-024-10155-3
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In insight problem-solving, the cognitive processes that help you solve a problem happen outside your conscious awareness. 4. Working backward. Working backward is a problem-solving approach often ...
14 types of problem-solving strategies. Here are some examples of problem-solving strategies you can practice using to see which works best for you in different situations: 1. Define the problem. Taking the time to define a potential challenge can help you identify certain elements to create a plan to resolve them.
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17 Effective Problem-Solving Strategies. Effective problem-solving strategies include breaking the problem into smaller parts, brainstorming multiple solutions, evaluating the pros and cons of each, and choosing the most viable option. ... then determine the tasks needed the week before, two weeks before, etc. all the way back to the present. 4 ...
4 steps to better problem solving. While it might be tempting to dive into a problem head first, take the time to move step by step. Here's how you can effectively break down the problem-solving process with your team: 1. Identify the problem that needs to be solved. One of the easiest ways to identify a problem is to ask questions.
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Problem solving strategies. Problem solving strategies are methods of approaching and facilitating the process of problem-solving with a set of techniques, actions, and processes.Different strategies are more effective if you are trying to solve broad problems such as achieving higher growth versus more focused problems like, how do we improve our customer onboarding process?
Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue. The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything ...
7 strategies for solving problems There are several different ways of approaching and handling a problem, depending on the nature of the challenge and the resources available to you. Choosing the most suitable strategy is crucial for devising a workable solution. Below is a list of seven strategies, methods and tips for problem-solving: Take ...
Problem-solving strategies explained, with examples Strategies to help you understand the problem. Clarify the problem. ... If you have two problems to solve, and one, such as developing a method of time travel, seems currently unsolvable, work on the other problem first. If you have two important problem-solving assignments, with one due ...
Balance divergent and convergent thinking. Ask problems as questions. Defer or suspend judgement. Focus on "Yes, and…" rather than "No, but…". According to Carella, "Creative problem solving is the mental process used for generating innovative and imaginative ideas as a solution to a problem or a challenge.
In project management and team collaboration, problem-solving is the process of identifying and resolving issues that arise during a project. It is a crucial skill that helps fix broken processes, improve performance, and identify opportunities. Problem-solving enables project managers and team leaders to overcome challenges and achieve success ...
Structured problem solving strategies can be used to address almost any complex challenge in business or public policy. ... What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a ...
Teams today aren't just asked to execute tasks: They're called upon to solve problems. You'd think that many brains working together would mean better solutions, but the reality is that too ...
1. Define the problem. Diagnose the situation so that your focus is on the problem, not just its symptoms. Helpful problem-solving techniques include using flowcharts to identify the expected steps of a process and cause-and-effect diagrams to define and analyze root causes.. The sections below help explain key problem-solving steps.
Problem Solving Strategy 6 (Work Systematically). If you are working on simpler problems, it is useful to keep track of what you have figured out and what changes as the problem gets more complicated. For example, in this problem you might keep track of how many 1 × 1 squares are on each board, how many 2 × 2 squares on are each board, how ...
Coping strategies can be divided into two types: problem-solving strategies involve actively reducing challenging ... adjustment indicated that there was no significant difference between the scores of problem-focused strategies (problem-solving and cognitive evaluation) in the control [14.200 ± 2.551] and experiment [14.233 ± ...
Collaborative cooperation (CC) and division of labor cooperation (DLC) are two prevalent forms of cooperative problem-solving approaches in daily life. Despite extensive research on the neural mechanisms underlying cooperative problem-solving approaches, a notable gap exists between the neural processes that support CC and DLC. The present study utilized a functional near-infrared spectroscopy ...
The blended synchronous classroom fosters equitable and balanced educational development; however, challenges persist, including low motivation and suboptimal effects on the deep learning of remote students. This study employs a student-centered learning approach and incorporates the process-oriented guided inquiry learning (POGIL) strategy into the blended synchronous science classroom. In ...