Abstract
Objective
This study was conducted to examine the relationship between midwifery and nursing students' cognitive flexibility levels and their decision-making styles.
Method
This descriptive study included 320 students enrolled in the midwifery and nursing departments at Lokman Hekim University in Ankara between February and June 2025. The study was completed with 302 students who were present at school on the day the data was collected and agreed to participate in the study. Data were collected using the “Personal Information Form,” “Cognitive Flexibility Inventory,” and “Melbourne Decision Making Scale I-II.” In addition to descriptive statistics, Pearson correlation and linear regression analyses were used in the analysis of the data.
Results
tudents' levels of cognitive flexibility were found to be high; careful decision-making was determined to be the most frequently used decision-making style. A positive and significant relationship was found between cognitive flexibility and self-esteem in decision-making (r = 0.540) and careful decision-making (r = 0.559), while negative and significant relationships were determined with avoidant, procrastinating, and panicked decision-making styles (p < 0.01). Regression analyses revealed that cognitive flexibility showed significant relationships with self-esteem in decision-making (R2 = 0.289) and careful decision-making styles (R2 = 0.311).
Conclusion
Students with high cognitive flexibility were found to prefer more careful and self-esteem-based decision-making styles and to avoid negative decision strategies. The findings indicate the importance of supporting cognitive flexibility in developing decision-making skills among students studying in the health field.
Keywords: Cognitive flexibility, Decision-making style, Midwifery, Nursing students, Self-esteem
Introduction
Cognitive flexibility is defined as an individual's capacity to adapt to new or unexpected situations, switch between different tasks, and evaluate alternative perspectives [5, 13]. Cognitive flexibility helps individuals adapt to changing conditions, generate solutions to problems they encounter, and cope with difficulties. In this respect, cognitive flexibility is an important skill that increases an individual's life satisfaction and contributes to their overall well-being. Conversely, individuals with low cognitive flexibility may prefer ineffective coping methods in problem-solving processes, which can lead to more frequent errors. Insufficient cognitive flexibility can negatively affect mental health by causing individuals to maintain dysfunctional thought processes [15]. Indeed, studies indicate that low cognitive flexibility is associated with repetitive thinking tendencies and depression [3, 28, 38]. In contrast, individuals with high cognitive flexibility are reported to cope more effectively with challenging situations and have higher levels of well-being [3, 18]. Furthermore, the ability to change the direction of cognition and adapt to environmental circumstances is thought to be developed through cognitive flexibility [36].
One of the most important aspects of cognitive flexibility is the individual's ability to make appropriate and effective decisions. The decision-making process is defined as the individual identifying various behavioral options, evaluating them, making a choice, and implementing that choice. This process may vary depending on the individual's approach to the problem, cognitive characteristics, experience, and environmental conditions. The methods used in the decision-making process and the solution paths followed when making a decision about a problem are defined as decision strategies. These strategies are influenced by various variables such as individual characteristics, the nature of the decision, level of knowledge, time allocated for decision-making, skills, and experience [12, 19]. It has been reported that individuals use careful, procrastinating, panicky, and evasive decision-making styles [12]. Research shows that various factors such as knowledge level, clinical experience, working conditions, and nurse-to-patient ratio affect nurses' decision-making skills [16, 25, 29].
Decision-making in healthcare, particularly in the form of clinical decision-making, is a complex process involving elements such as information processing, critical thinking, evaluation of evidence, problem-solving skills, reflective thinking, and clinical assessment. This process is extremely important in protecting individuals' health, minimizing risks, and determining the most appropriate care interventions [23]. Known as the clinical decision-making process, it requires nurses to quickly assess conditions, analyze information, and evaluate effectiveness while implementing decisions to provide effective care [1, 2, 4]. Therefore, the ability of healthcare professionals to make accurate and effective decisions is related to their high cognitive competence and cognitive flexibility levels.
A review of the literature reveals that numerous studies have shown that nurses with high cognitive flexibility cope more effectively with the difficulties they encounter in clinical practice, are more successful in patient communication, and have lower levels of work-related stress [8, 9, 22, 37]. Similarly, students studying in the health field have the opportunity to develop their decision-making skills and gain professional experience during clinical practice [7, 21]. Today's rapidly changing healthcare environment increases the need for healthcare professionals who not only have a wealth of knowledge but also possess higher-level cognitive skills such as critical thinking, problem solving, and decision making [32, 33]). In this context, the cognitive flexibility levels of midwifery and nursing students, who will be the healthcare professionals of the future, are extremely important in managing complex patient situations, adapting to changing clinical conditions, and implementing evidence-based practices [17, 37].
When studies with samples consisting of nursing and midwifery students are examined, it is seen that either only the decision-making styles or only the levels of cognitive flexibility of the students are addressed [12, 27]. In light of these findings, it is noteworthy that studies examining cognitive flexibility and decision-making styles in nursing and midwifery students are limited. Furthermore, no studies have been found that address the relationship between the cognitive flexibility levels and decision-making styles of nursing and midwifery students. Accordingly, our study aims to determine the relationship between the cognitive flexibility levels and decision-making skills of midwifery and nursing students and to examine the factors that influence this relationship. It is believed that the results of our study, which reveal the relationship between the cognitive flexibility levels and decision-making skills of midwives and nurses working in all areas of healthcare during their undergraduate education, will contribute significantly to making correct and effective clinical decisions and thus to providing quality healthcare services.
Materials and methods
Research design
This study was designed as a descriptive and exploratory research. The research was conducted to determine the relationship between the cognitive flexibility levels and decision-making styles of midwifery and nursing students.
Research location and time
The research was conducted in the Nursing and Midwifery Departments of the Faculty of Health Sciences at Lokman Hekim University. The data collection process was completed between February 2025 and June 2025.
Research population and sample
The study included 320 students enrolled in the midwifery and nursing departments at Lokman Hekim University in Ankara. Participation in the study was voluntary, and no sampling method was applied. Therefore, in this study, a power analysis to determine the sample size prior to data collection was not conducted, as the aim was to reach the entire target population. The study was completed with 302 students who were present at the school on the day the data was collected and who agreed to participate in the study. Approximately 94.3% of the enrolled students participated in the study. Data were collected based on participants' self-reports. Data were collected from students enrolled in the nursing department by faculty members working in the midwifery department, and from students enrolled in the midwifery department by faculty members working in the nursing department. The faculty members who collected the data did not teach any courses in the departments where they conducted the data collection process. The data collection tools were physically distributed to the students in a face-to-face setting, and the forms were filled out by the participants on a voluntary basis. Data collection was carried out by visiting each class only once, thus preventing participants from filling out the measurement tools more than once.
Inclusion criteria
Actively continuing the education and training process,
Being at school on the day of the research.
Giving voluntary consent to participate in the research.
Exclusion criteria
Those who were not at school on the day the data was collected.
Those who filled out the data incompletely.
Data collection tools
Data were collected using the “Student Descriptive Characteristics Form,” the “Cognitive Flexibility Inventory,” and the “Melbourne Decision Making Scale I-II,” which were developed based on a literature review.
Student descriptive characteristics form
This form was developed by the researchers based on a literature review. It includes students' sociodemographic characteristics such as age, gender, grade level, department, income status, parental education level, and reasons for choosing their department.
Cognitive Flexibility Inventory (CFI)
Developed by Dennis and Vander Wal [11] to assess individuals' levels of cognitive flexibility. The Turkish adaptation of the CFI was carried out by Sapmaz and Doğan. The Turkish form retains the two-factor structure and 20-item format of the original inventory, but uses a 5-point Likert-type rating scale. It is scored as follows: “Not at all appropriate” (1), “Not appropriate” (2), “Somewhat appropriate” (3), ‘Appropriate’ (4), “Completely appropriate” (5). Therefore, total scores in the Turkish version range from 20 to 100, with higher scores indicating greater cognitive flexibility. Items 2, 4, 7, 9, 11, and 17 of the scale are reverse-coded. Three different types of scores can be obtained from the scale: the total cognitive flexibility score, the “alternatives” subscale score, and the “control” subscale score. High scores indicate high cognitive flexibility. The characteristics of individuals with high cognitive flexibility can be listed as the ability to make decisions independently, high self-esteem, the ability to view events from different perspectives, internal locus of control, lower levels of depression, and optimism. In terms of reliability, the Cronbach's alpha internal consistency coefficient of the Turkish form was calculated as 0.90, indicating a high level of internal consistency. In the present study, the Cronbach’s alpha coefficient for the total score of the Cognitive Flexibility Inventory was found to be 0.915. The reliability coefficient of the “Alternatives” subscale was 0.899, while that of the “Control” subscale was 0.882.
Melbourne Decision-Making Scale I-II (MDMS I-II)
Developed by Mann et al. [20] and adapted into Turkish by Deniz [10]. The scale consists of two parts: the first part aims to determine self-esteem in decision-making, while the second part consists of four sub-dimensions. The MKVÖ I scale, which determines the level of self-esteem (self-confidence) in decision-making, consists of six items, three of which are scored in a positive direction and three in a negative direction. The maximum possible score on the scale is 12. High scores indicate high self-esteem in decision-making. The MKVÖ II scale consists of 22 items and has four sub-dimensions. These are: careful decision-making style (6 items), avoidant decision-making style (6 items), procrastinating decision-making style (5 items), and panicky decision-making style (5 items). Higher scores indicate that the relevant decision-making style is used more frequently. The subscales of the MKVÖ II scale are interpreted as follows.
Careful decision-making style: This is when a person carefully seeks information before making a decision and makes a choice after carefully considering the options.
Avoidant decision-making style: This is when a person avoids making decisions, tends to leave necessary decisions to others, and thus tries to avoid making decisions by shifting responsibility to others.
Procrastinatory decision-making style: This is when a person continuously postpones or delays decision-making without a valid reason.
Panic decision-making style: This is when a person feels pressured for time during the decision-making process, acts hastily in making decisions, and strives to reach a solution quickly.
The Cronbach's alpha value of the Melbourne Decision Making Scale ranged from 0.70 to 0.81 in the adaptation study. In this study, the internal consistency coefficient of the “Self-Esteem in Decision Making” subscale, which constitutes the first section of the Melbourne Decision Making Scale, is 0.90. The sub-dimensions related to decision-making styles in the second section also have high reliability levels. The Alpha value was calculated as 0.87 for “Careful Decision-Making Style,” 0.86 for “Avoidant Decision-Making Style,” 0.88 for “Procrastinating Decision-Making Style,” and 0.84 for “Panic Decision-Making Style.”
Data collection process
The data collection process was conducted by researchers using face-to-face interviews. The students participating in the study were informed about the study and asked to sign an informed consent form. Incomplete forms were not included in the analysis.
Data analysis
The data obtained in the study were analyzed using IBM SPSS Statistics for Windows, Version 22.0 (SPSS Inc., Chicago, IL, USA). Frequency and percentage analyses were used to determine participants’ descriptive characteristics, and means and standard deviations were calculated to examine scale scores. To determine whether the study variables exhibited a normal distribution, Skewness and Kurtosis values were examined. In the literature, values between +1.5 and −1.5 are accepted as indicators of normal distribution [30]. Based on these findings, the variables were determined to be normally distributed, and parametric methods were employed in the analyses. The relationships among the scale dimensions were examined using Pearson correlation and linear regression analyses. Statistical assumptions related to regression analyses were also evaluated. The independence of residuals was tested using the Durbin–Watson statistic. The homoscedasticity assumption was assessed by examining scatterplots of standardized residuals (ZRESID) against standardized predicted values (ZPRED), and residuals were observed to be randomly and homogeneously distributed across predicted values without a systematic pattern. The multicollinearity assumption was evaluated using Variance Inflation Factor (VIF) and Tolerance values. Because the regression models included a single independent variable, Tolerance and VIF values were calculated as 1.00, indicating that there was no multicollinearity problem in the models. Independent samples t-tests, one-way analysis of variance (ANOVA), and post hoc (LSD) analyses were used to examine differences in scale scores according to participants’ descriptive characteristics.
Ethical approval
Ethical approval for the research was obtained from the Scientific Research Ethics Committee of Lokman Hekim University (Date: 31.01.2025, Decision No: 2025/1, Code No: 2024320). Participants were informed about the purpose and scope of the research, and written informed consent was obtained. The research was conducted in accordance with the principles of the Declaration of Helsinki.
Results
The findings related to the descriptive characteristics of the students are presented below.
Table 1 presents the general distribution of the sociodemographic and educational characteristics of the students participating in the study. It is seen that the sample consists predominantly of female students and that the participants are enrolled in midwifery and nursing programs. It is noteworthy that a large proportion of the students are not employed and describe their income situation as balanced with their expenses. When examining the educational levels of the parents, it is seen that secondary education stands out for both parent groups. The majority of participants stated that they chose their departments voluntarily, and it is understood that employment opportunities were a decisive factor in their department selection. The age distribution of the students is concentrated in young adulthood.
Table 1.
Distribution of Students According to Descriptive Characteristics (n:302)
| Descriptive Characteristics | Frequency (n) | Percentage (%) |
|---|---|---|
| Gender | ||
| Male | 24 | 7.9 |
| Female | 278 | 92.1 |
| Department | ||
| Midwifery | 116 | 38.4 |
| Nursing | 186 | 61.6 |
| Employment Status | ||
| Working | 23 | 7.6 |
| Not working | 279 | 92.4 |
| Income Status | ||
| Income less than expenses | 13 | 4.3 |
| Income equal to expenses | 238 | 78.8 |
| Income more than expenses | 51 | 18.9 |
| Mother's Education Level | ||
| Primary school | 77 | 25.5 |
| Secondary school | 135 | 44.7 |
| University and above | 90 | 29.8 |
| Father's Education Level | ||
| Primary school | 38 | 12.6 |
| Secondary school | 147 | 48.7 |
| University and above | 117 | 38.7 |
| Chose Department Willingly | ||
| Yes | 227 | 75.2 |
| No | 75 | 24.8 |
| Reason for Choosing the Department | ||
| Family's wish | 25 | 8.3 |
| Desire to help people | 45 | 14.9 |
| Ease of starting a professional career | 145 | 48.0 |
| Feeling close to the profession | 78 | 25.8 |
| By coincidence | 9 | 3.0 |
| Mean(SD) | Min- Max | |
| Age (Mean ± SD) | 20.93 ± 2.51 | 18–38 |
Table 2 shows the score distributions related to students' cognitive flexibility and decision-making styles. The findings reveal that cognitive flexibility generally ranged from moderate to high levels in the sample and that there was a balanced distribution in both subdimensions. When examining decision-making styles, it is noteworthy that functional decision-making styles (particularly careful decision-making and self-esteem in decision-making) are relatively more dominant, while dysfunctional styles such as avoidant, procrastinating, and panicked decision-making are observed at lower levels.
Table 2.
Mean Scores of Cognitive Flexibility and Decision-Making Styles Scales and Their Subdimensions
| Scale/Subdimension | Mean (M) | SD | Min | Max |
|---|---|---|---|---|
| Total Cognitive Flexibility | 74.162 | 9.433 | 49.000 | 100.000 |
| Alternatives | 50.344 | 6.784 | 32.000 | 65.000 |
| Control | 23.818 | 4.244 | 10.000 | 35.000 |
| Decision-Making Self-Esteem | 8.990 | 2.177 | 3.000 | 12.000 |
| Careful Decision-Making Style | 9.225 | 2.401 | 0.000 | 12.000 |
| Avoidant Decision-Making Style | 4.136 | 2.765 | 0.000 | 12.000 |
| Procrastinating Decision-Making Style | 3.748 | 2.468 | 0.000 | 10.000 |
| Panic Decision-Making Style | 4.066 | 2.385 | 0.000 | 10.000 |
Table 3 shows the relationships between cognitive flexibility and decision-making styles. The findings reveal that cognitive flexibility is positively related to functional decision-making styles and negatively related to dysfunctional decision-making styles. In particular, meaningful and consistent relationships were observed between cognitive flexibility and self-esteem and careful decision-making styles in decision-making; conversely, inverse relationships were found between cognitive flexibility and avoidant, procrastinating, and panicked decision-making styles. Examinations at the sub-dimension level show that both the Alternatives and Control dimensions are related to functional decision-making styles, while exhibiting negative relationships with dysfunctional decision-making tendencies. This pattern suggests that cognitive flexibility co-occurs with functional decision-making styles in the decision-making process.
Table 3.
Correlation Analysis Between Cognitive Flexibility and Decision-Making Styles Scores
| Total Cognitive Flexibility | Alternatives | Control | ||
|---|---|---|---|---|
| Decision-Making Self-Esteem | r | 0.540** | 0.398** | 0.563** |
| p | < 0.001 | < 0.001 | < 0.001 | |
| Careful Decision-Making Style | r | 0.559** | 0.528** | 0.399** |
| p | < 0.001 | < 0.001 | < 0.001 | |
| Avoidant Decision-Making Style | r | −0.451** | −0.349** | −0.446** |
| p | < 0.001 | < 0.001 | < 0.001 | |
| Procrastinating Decision-Making Style | r | −0.439** | −0.305** | −0.488** |
| p | < 0.001 | < 0.001 | < 0.001 | |
| Panic Decision-Making Style | r | −0.436** | −0.283** | −0.517** |
| p | < 0.001 | < 0.001 | < 0.001 |
Pearson correlation analysis; p < 0.001
Table 4 shows the relationship between cognitive flexibility and different decision-making styles. The findings reveal that cognitive flexibility has positive and significant relationships with functional decision-making styles and negative and significant relationships with non-functional decision-making styles. In particular, cognitive flexibility is associated with self-esteem and careful decision-making styles in decision-making and explains a significant portion of the variance. In contrast, inverse relationships were found between cognitive flexibility and avoidant, procrastinating, and panicked decision-making styles. The overall fit indices of the models indicate that the analysis assumptions were met and that the findings are statistically reliable.
Table 4.
The Relationship Between Cognitive Flexibility and Decision-Making Styles
| Independent Variable | Unstandardized Coefficients | Standardized Coefficients | t | p | 95% Confidence Interval | ||
|---|---|---|---|---|---|---|---|
| B | SE | β | Lower | Upper | |||
| Cognitive Flexibility | 0.125 | 0.011 | 0.540 | 11.109 | < 0.001 | 0.103 | 0.147 |
| *Dependent Variable = Decision-Making Self-Esteem, R = 0.540; R2 = 0.289; F = 123.400; p < 0.001; Durbin-Watson = 2.035 | |||||||
| Cognitive Flexibility | 0.142 | 0.012 | 0.559 | 11.689 | < 0.001 | 0.118 | 0.166 |
| *Dependent Variable = Careful Decision-Making Style, R = 0.559; R2 = 0.311; F = 136.635; p < 0.001; Durbin-Watson = 2.061 | |||||||
| Cognitive Flexibility | −0.132 | 0.015 | −0.451 | −8.764 | < 0.001 | −0.162 | −0.103 |
| *Dependent Variable = Avoidant Decision-Making Style, R = 0.451; R2 = 0.201; F = 76.809; p < 0.001; Durbin-Watson = 1.897 | |||||||
| Cognitive Flexibility | −0.115 | 0.014 | −0.439 | −8.459 | < 0.001 | −0.142 | −0.088 |
| *Dependent Variable = Procrastinating Decision-Making Style, R = 0.439; R2 = 0.190; F = 71.552; p < 0.001; Durbin-Watson = 1.886 | |||||||
| Cognitive Flexibility | −0.110 | 0.013 | −0.436 | −8.392 | < 0.001 | −0.136 | −0.084 |
| *Dependent Variable = Panic Decision-Making Style, R = 0.436; R2 = 0.187; F = 70.432; p < 0.001; Durbin-Watson = 2.112 | |||||||
Overall, it can be said that cognitive flexibility is associated with the decision-making process and co-occurs with functional decision-making styles; conversely, it is a parameter associated with lower levels of dysfunctional decision-making tendencies.
Dıscussıon
In this study, the association between cognitive flexibility levels and decision-making styles among midwifery and nursing students was examined and interpreted in relation to the existing literature. The participant sample predominantly comprised female students, with most participants enrolled in midwifery and nursing programs. The majority of students reported income levels that were generally balanced with their expenses, and parental education levels were primarily concentrated at the secondary education level.
The study found that the students' average total score on the Cognitive Flexibility Inventory was 74.162 ± 9.433, indicating that they generally possessed a high level of cognitive flexibility. This finding shows that the students have strong abilities to adapt to environmental changes, develop different solutions, and continue their learning processes. Based on previous studies, it is thought that the high cognitive flexibility of students in young adulthood stems from their active cognitive development and ability to develop effective coping methods [6, 14, 24, 34, 35]. The high average score on the “Alternatives” subscale of the students' cognitive flexibility scale and the moderate average score on the “Control” subscale indicate that students can approach the difficulties they encounter from different perspectives and manage their cognitive processes effectively. This result is consistent with the findings of Sapmaz and Doğan [26], who stated that individuals with high cognitive flexibility have more effective coping and alternative thinking skills in stressful situations. This situation reveals that university students' cognitive flexibility is extremely important in terms of problem-solving skills.
When examining the findings related to students' decision-making styles, it is seen that significant scores were obtained in the dimensions of self-esteem and careful decision-making. This situation shows that cognitive flexibility supports planned, self-regulated, and responsibility-based functional strategies. Furthermore, a positive relationship was found between cognitive flexibility and careful decision-making, while a negative relationship was found between panic, avoidance, and procrastination decision-making styles. This result reveals that individuals with high cognitive flexibility tend to make planned, self-respecting, and controlled decisions. Studies by Tatlılıoğlu [31] and Hüseyniklioğlu and Tüysüz [12] also reported that cognitive flexibility is positively related to decision-making styles. This suggests that cognitive flexibility is not only a cognitive skill but also a personal characteristic that supports self-efficacy, self-confidence, and emotional balance in decision-making processes. The findings reveal a positive relationship between cognitive flexibility and self-esteem and careful decision-making. This relationship suggests that individuals with higher cognitive flexibility may be more confident in their decision-making processes and adopt a more careful approach. At the same time, cognitive flexibility enhances decision quality by enabling individuals to quickly shift their thinking direction in changing circumstances and develop alternative solutions. Tatlılıoğlu [31] and Kruczek et al. [18] state that cognitive flexibility strengthens self-esteem in decision-making processes and increases the ability to cope with uncertainty. These findings are consistent with the results of the current study. However, there are significant negative correlations between cognitive flexibility and avoidant, procrastinating, and panicked decision-making styles. This finding suggests that individuals with higher cognitive flexibility may be less prone to indecision, avoidance, and panic reactions in their decision-making processes and may manage the process in a more balanced manner. It is known that cognitive rigidity, the opposite of cognitive flexibility, increases emotional reactivity in decision-making processes and weakens rational evaluation [15, 38]. From this perspective, the negative relationships in the study indirectly support the negative effect of cognitive rigidity on decision-making functionality. The study also shows that the sub-dimensions of cognitive flexibility, “alternatives” and “control,” have meaningful relationships with decision-making styles. In particular, the inverse relationships between the control sub-dimension and panic, procrastination, and avoidance decision-making styles suggest that individuals with higher control perceptions may be less prone to dysfunctional decision-making strategies. This situation indicates that an individual's perception of being able to manage their cognitive processes may be related to the quality of their decision-making behavior. This result is also consistent with the findings of Açıkgöz and Karaca [3], who showed that individuals with high cognitive flexibility can make more effective decisions in stressful environments. The regression findings reveal that cognitive flexibility is a variable related to different decision-making styles. It is assessed that an increase in cognitive flexibility may be associated with self-esteem and a careful approach in the decision-making process, while a decrease in dysfunctional tendencies such as avoidance, procrastination, and panic may also be related. This suggests that individuals with higher levels of cognitive flexibility may exhibit a more self-regulated, planned, and self-confident approach in their decision-making processes; conversely, dysfunctional decision-making tendencies may be lower. The moderate explanatory power of the model indicates that cognitive flexibility is an important determinant of decision-making styles. These findings support that cognitive flexibility is not only a cognitive skill but is also related to emotional regulation and behavioral self-control [9, 22].
In conclusion, this study presents important findings regarding the relationship between cognitive flexibility and decision-making styles among midwifery and nursing students. The results indicate that decision-making processes should be considered alongside cognitive flexibility as a critical variable. Accordingly, it is recommended that intervention and educational initiatives be designed to support students’ decision-making skills. Furthermore, to enhance the generalizability of the findings, future research should be conducted with larger sample sizes and include both public and private universities.
Limitations
This study has some limitations. The fact that the research was conducted at a single university and that the sample consisted of midwifery and nursing students, resulting in a sample largely composed of female participants, are among the limitations of the study.
Acknowledgements
The authors would like to thank all students who voluntarily participated in this study.
Authors’ contributions
DSK: Conceptualization, Methodology, Supervision, Writing – Review & Editing. IM: Conceptualization, Data Curation, Formal Analysis, Writing – Original Draft. SKM: Conceptualization, Data Curation, Formal Analysis, Writing – Review & Editing, Corresponding Author. All authors read and approved the final manuscript.
Funding
This research received no specific grant from any funding agency, institution, or organization.
Data availability
The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study was approved by the Lokman Hekim University Scientific Research Ethics Committee (Date: 31.01.2025; Decision No: 2025/1; Code: 2024320). Informed written consent was obtained from all participants. The study was conducted in accordance with the principles of the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Abate HK, Birhanu Y, Gebrie MH. Clinical decision-making approaches and associated factors among nurses working in a tertiary teaching hospital. Int J Afr Nurs Sci. 2022;17:100432. [Google Scholar]
- 2.Aboalrob W, Ayed A, Malak MZ, Aqtam I. Understanding the influence of self-concept on clinical decision-making among nurses: a cross-sectional study. PLoS ONE. 2025;20(8):e0330905. 10.1371/journal.pone.0330905. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Acikgoz F, Karaca A. The effect of a mindfulness-based cognitive therapy program on depression, anxiety, stress, and cognitive flexibility in nursing students: a randomized clinical trial. J Prof Nurs. 2025;56:94–103. 10.1016/j.profnurs.2024.12.005. [DOI] [PubMed] [Google Scholar]
- 4.Ayed A. The relationship between emotional intelligence and clinical decision-making among nurses in neonatal intensive care units. SAGE Open Nursing. 2025;11:1–8. 10.1177/23779608251321352. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Bonnici H. Cognitive flexibility in everyday life: understanding its importance. Cambridge, United Kingdom: Cambridge University Press; 2020. 10.3389/fpsyg.2024.1420272. [Google Scholar]
- 6.Camcı Erdoğan S. Investigation of cognitive flexibility levels of gifted education candidates. Celal Bayar Univ J Soc Sci. 2018;16:77–96. 10.18026/cbayarsos.465710.
- 7.Coram C. Expert role modeling effect on novice nursing students’ clinical judgment. Clin Simul Nurs[Internet]. 2016;12(9):385–91. 10.1016/j.ecns.2016.04.009. [Google Scholar]
- 8.Çataldaş SK, Atkan F, Eminoğlu A. The effect of psychodrama-based intervention on therapeutic communication skills and cognitive flexibility among nursing students: A 12-month follow-up study. Nurse Educ Pract. 2024;80:S1471-5953(24)00247-6. 10.1016/j.nepr.2024.104118. [DOI] [PubMed]
- 9.Dehghani F, Bahari Z. The mediating role of cognitive flexibility in the relationship between job stress and psychological wellbeing of nurses. IJN. 2022;34:16–27. 10.32598/ijn.34.5.2. [Google Scholar]
- 10.Deniz ME. Investigation of the relation between decision making self-esteem, decision making style and problem solving skills of university students. Eurasian Journal of Educational Research (Ejer). 2004;15(13):23–35. [Google Scholar]
- 11.Dennis JP, Wal JSV. The cognitive flexibility inventory: instrument development and estimates of reliability and validity. Cognit Ther Res. 2010;34(3):241–53. 10.1007/s10608-009-9276-4. [Google Scholar]
- 12.Hüseyniklioğlu B, Tüysüz F. Examining the relationship between nursing and midwifery students' self-perceptions of leadership behaviors and their decision-making styles. J Health Nurs Manag. 2023;10(1):38–48. 10.54304/SHYD.2023.39206.
- 13.Karakuş İ. University students’ cognitive flexibility and critical thinking dispositions. Front Psychol. 2024;15:1420272. 10.3389/fpsyg.2024.1420272. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kazu H, Pullu S. Cognitive flexibility levels and self-efficacy perceptions of preservice teachers. Discourse Commun Sustain Educ. 2023;14:36–47. 10.2478/dcse-2023-0004. [Google Scholar]
- 15.Kelly AC, Zuroff DC, Foa CL, Gilbert P. Who benefits from training in self-compassionate self-regulation? A study of smoking reduction. J Soc Clin Psychol. 2010;29(7):727–55. 10.1521/jscp.2010.29.7.727. [Google Scholar]
- 16.Khallaf H, Abu Ejheisheh M, Malak MZ, Shehadeh A, Ayed A, Batran A, et al. Nurses’ knowledge, attitudes, and decision-making related to sepsis assessment and management in Palestinian intensive care units. BMC Nurs. 2025;24:779. 10.1186/s12912-025-03341-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kılıç Z, Uzdil N, Günaydın Y. The effect of cognitive flexibility on nurses’ attitudes toward professional autonomy. Nurs Ethics. 2024;31:321–30. 10.1177/09697330231174533. [DOI] [PubMed] [Google Scholar]
- 18.Kruczek A, Basińska MA, Janicka M. Cognitive flexibility and coping flexibility in nurses: The moderating role of age, seniority, and perceived stress. Int J Occup Med Environ Health. 2020;33(4):507–521. 10.13075/ijomeh.1896.01567. [DOI] [PubMed]
- 19.Kuzgun Y. Career development and counseling (4th. rev. Ankara, Türkiye: Nobel Publishing; 2014. [Google Scholar]
- 20.Mann L, Burnett P, Radford M, Ford S. The Melbourne decision making questionnaire: an instrument for measuring patterns for coping with decisional conflict. Behav Decis Making. 1997;10(1):1–19. 10.1002/SICI.1099-0771(199703). [Google Scholar]
- 21.Marques MFM. Decision making from the perspective of nursing students. Rev Bras Enferm. 2019;72(4):1102–8. 10.1590/0034-7167-2018-0311. [DOI] [PubMed]
- 22.Mehralian G, Yusefi AR, Bahmaei J, et al. Ethical intelligence and cognitive flexibility among nurses and their role in predicting the level of patient privacy protection. BMC Nurs. 2024;23:501. 10.1186/s12912-024-02153-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Novalia A, Rachmi SF, Yetti K. Clinical decision-making among bachelor’s and clinical internship (professional) nursing students in Indonesia. J Public Health Res. 2022;11:2735. 10.4081/jphr.2021.2735. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Pepe Ş. The relationship between academic self-efficacy and cognitive flexibility: physical education and sports teacher candidates. Propósitos y Representaciones. 2021;9:113. 10.20511/pyr2021.v9nSPE3.1159. [Google Scholar]
- 25.Rahman NA, Chan CM, Zakaria MI, Jaafar MJ. Knowledge and attitudes toward identification of systemic inflammatory response syndrome (SIRS) and sepsis among emergency personnel in a tertiary teaching hospital. Australasian Emergency Care. 2019;22(1):13–21. 10.1016/j.auec.2018.11.002. [DOI] [PubMed] [Google Scholar]
- 26.Sapmaz F, Doğan T. Assessment of cognitive flexibility: Reliability and validity studies of the Turkish version of the Cognitive Flexibility Inventory. Ankara University Journal of Educational Sciences. 2013;46:143–61. 10.1501/Egifak_0000001278. [Google Scholar]
- 27.Sarpdağı Y, Kaplan E, Sir Ö, Yıldız M, Kaymaz D, Çiftci N, Sarpdaği S. The Effect of Secondary Traumatic Stress and Cognitive Flexibility on Psychological Well Being in Health Education Students. BMC Med Educ. 2025;25(1):929. 10.1186/s12909-025-07489-w. [DOI] [PMC free article] [PubMed]
- 28.Shapero BG, Greenberg J, Mischoulon D, Pedrelli P, Meade K, Lazar SW. Mindfulness-based cognitive therapy improves cognitive functioning and flexibility among individuals with elevated depressive symptoms. Mindfulness. 2018;9(5):1457–69. 10.1007/s12671-018-0889-0. [Google Scholar]
- 29.Storozuk SA, MacLeod MLP, Freeman S, Banner D. A survey of sepsis knowledge among Canadian emergency department registered nurses. Australas Emerg Care. 2019;22(2):119–25. 10.1016/j.auec.2019.01.007. [DOI] [PubMed] [Google Scholar]
- 30.Tabachnick BG, Fidell LS. Using multivariate statistics. 6th ed. Boston, MA: Pearson; 2013. [Google Scholar]
- 31.Tatlılıoğlu K. An examination of the relationship between self-esteem in decision making and decision-making styles among university students based on selected variables. J Acad Soc Res. 2014;2(1):150–170. 10.16992/ASOS.46.
- 32.Temel S. The effects of problem-based learning on pre-service teachers’ critical thinking dispositions and perceptions of problem-solving ability. S Afr J Educ. 2014;34(1):1–20. 10.15700/201412120936.
- 33.Thabet M, Taha EE-S, Abood SA, Morsy SR. The effect of problem-based learning on nursing students’ decision-making skills and styles. J Nurs Educ Pract. 2017;7(6). 10.5430/jnep.v7n6p108.
- 34.Toraman Ç, Aytuğ Koşan AM, Yurdal MO. Cognitive flexibility levels, learning approaches, and learning strategies used by medical faculty students (Çanakkale sample). World Med Educ. 2020;19:76–97. 10.25282/ted.589099.
- 35.Yazgan AD. Examining the relationship between cognitive flexibility levels and cultural intelligence levels of prospective teachers. Ahi Evran Univ J Soc Sci Inst. 2021;7:212–231. 10.31592/aeusbed.803469.
- 36.Yelpaze İ. Cognitive flexibility as a predictor of psychological resilience among university students: The mediating role of self-compassion. Erzincan Univ J Educ Fac. 2020;22(2):535–549. 10.17556/erziefd.631767.
- 37.Yurt E, Hayli ÇM. Mediating role of self-efficacy and cognitive flexibility in the relationship between critical thinking and positive mental health in Turkish nursing students: a cross-sectional study. BMJ Open. 2025;15:e097631. 10.1136/bmjopen-2024-097631. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Zou Y, Li P, Hofmann SG, Liu X. The mediating role of non-reactivity in mindfulness training and cognitive flexibility: A randomized controlled trial. Front Psychol. 2020;11:1053. 10.3389/fpsyg.2020.01053. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.