Number of Citations: 0

Submitted: 20 September 2025
Accepted: 13 July 2026
Published online: 6 October, TAPS 2026, 11(4), 44-53
https://doi.org/10.29060/TAPS.2026-11-4/OA3885

Kasana Raksamani, Wathinee Phaknam, Sahatsa Mandee, Nuanprae Kitisin & Tachawan Jirativanont

Department of Anesthesiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Thailand

Abstract

Introduction: The multiple mini-interview (MMI) is widely used in residency selection to assess non-cognitive competencies, whilst Key Feature Questions (KFQs) evaluate clinical reasoning at critical decision points. Understanding associations between these tools may enhance candidate evaluation.

Methods: This retrospective study included 122 anaesthesiology residency candidates from three consecutive cohorts (2023–2025). MMI scores across five domains (motivation, crisis management, stress/mood management, situational awareness and decision-making, and growth mindset), grade point average (GPA), multiple-choice question (MCQ) scores, and KFQ scores were analysed. Spearman’s correlation examined associations with KFQ performance, and multiple linear regression identified independent contributors.

Results: The mean candidate age was 27.9 years (±1.98); 76.2% were female. Moderately strong positive correlations were observed between KFQ scores and MMI domains of motivation (r = 0.577, p < 0.001), crisis management (r = 0.511, p < 0.001), and situational awareness and decision-making (r = 0.658, p < 0.001). GPA and MCQ scores showed no significant associations with KFQ performance. Regression analysis explained 56.8% of the variance in KFQ scores (R² = 0.568, p < 0.001), with motivation (B = 0.361, p < 0.001), crisis management (B = 0.300, p = 0.016), and decision-making (B = 0.621, p < 0.001) as significant contributors.

Conclusion: Key MMI domains – motivation, crisis management, and situational awareness and decision-making – were significantly associated with clinical reasoning performance as measured by KFQs, whilst GPA and MCQ scores were not. These findings support integrating structured non-cognitive assessments alongside cognitive measures in anaesthesia residency selection, highlighting the value of a multi-dimensional approach to candidate evaluation.

Keywords:           Multiple Mini-Interview (MMI), Clinical Reasoning, Key Feature Questions (KFQ), Residency Selection, Anaesthesiology Education, Non-cognitive Competencies, Situational Awareness, Decision-making, Crisis Management, Academic Performance

Practice Highlights

• Situational awareness and decision-making showed the strongest association with clinical reasoning performance, underscoring their central role in anaesthesiology practice.
• Crisis management and motivation were also significantly associated with clinical reasoning ability, suggesting that adaptability and intrinsic drive support effective problem-solving in clinical contexts.
• Traditional academic measures (GPA and MCQ scores) were not significantly associated with clinical reasoning performance, highlighting their limitations as sole criteria for residency selection.
• Key Feature Questions (KFQs) complemented the MMI by capturing applied reasoning in structured, context-specific clinical scenarios.
• Integrating cognitive and non-cognitive assessments in residency selection may better identify candidates suited to the complex demands of anaesthesiology and inform competency-focused training.

MCQ, KFQ, and each of the five MMI station scores were standardised to a maximum of 100 points.

Table 1. Participant characteristics and academic performance (n = 122).

Figure 1. Violin plots showing score distributions for MMI competencies, GPA, MCQ, and KFQ. White dots indicate medians, thick bars interquartile ranges, and thin lines overall ranges.

Table 3. Multiple linear regression predicting clinical reasoning performance (KFQ Scores).

IV. DISCUSSION

This study examined the associations between multiple mini-interview (MMI) competencies and clinical reasoning performance, as assessed by Key Feature questions (KFQs), among anaesthesia residency candidates within the same selection process. Three MMI domains – motivation, crisis management, and situational awareness and decision-making –  showed significant positive associations with KFQ scores, with situational awareness and decision-making demonstrating the strongest relationship. In contrast, GPA, MCQ scores, and the other MMI domains (stress and mood management, growth mindset) were not significantly associated with KFQ performance. These findings highlight the value of targeted non-cognitive assessments as a complement to traditional academic measures in residency selection.

Situational awareness and decision-making were the most powerful predictors of clinical reasoning, underscoring their central role in anaesthesiology practice. These competencies align directly with the specialty’s demands, where anaesthesiologists must continuously monitor dynamic environments, recognise subtle changes in patient status, and anticipate potential complications (Flin et al., 2010; Schulz et al., 2013). Situational awareness involves perceiving relevant cues, interpreting their meaning, and projecting their implications, while decision-making requires selecting appropriate actions under time pressure (Stiegler & Tung, 2014). Together, these processes reflect the dual-process theory of cognition, in which clinicians alternate between analytic reasoning – deliberate, evidence-based thinking–and heuristic reasoning – fast, experience-based judgements (Gaba et al., 1995; Schulz et al., 2013; Stiegler & Tung, 2014). The strong association between situational awareness and decision-making with KFQ performance is unsurprising, as KFQs assess the application of knowledge to context-specific problems, requiring candidates to integrate information, weigh alternatives, and justify decisions in a structured format (Hrynchak et al., 2014). Thus, performance on KFQs is likely to mirror the cognitive operations underpinning high-quality decision-making in clinical practice (Naz et al., 2025).

Crisis management also emerged as a significant predictor of reasoning performance. This domain assesses a candidate’s ability to respond to acute, high-stakes scenarios by rapidly evaluating evolving situations, prioritising competing tasks, and implementing interventions under uncertainty  (Burden, 2020; Gaba et al., 1995; Minehart & Katz, 2021; Norman et al., 2017). These cognitive processes parallel the skills required in KFQs, which demand flexible thinking and the capacity to select appropriate management strategies when faced with critical problems (Hrynchak et al., 2014). Importantly, success in crisis management reflects not only technical knowledge but also the ability to remain composed, process information efficiently, and adapt decisions to rapidly changing conditions – all qualities that underpin effective clinical reasoning in anaesthesiology (Stiegler & Gaba, 2015).

Motivation to pursue anaesthesiology was another predictor of KFQ performance, highlighting the influence of intrinsic drive on reasoning ability. Candidates motivated by a genuine interest in the specialty may demonstrate greater perseverance, focus, and cognitive engagement when solving complex problems (Gaba, 2004; Niburski & Buu, 2021). Motivation may also enhance metacognitive skills, such as self-reflection and adaptive information processing, which are critical for reasoning in high-stress or ambiguous clinical situations (Flin & Patey, 2011; Flin et al., 2010; Reale et al., 2023). This finding suggests that motivation is not merely a personal attribute but an enabler of cognitive adaptability and resilience, allowing individuals to sustain effort and maintain reasoning performance even under demanding conditions.

Together, these results highlight that clinical reasoning ability is not solely dependent on factual knowledge but is shaped by a combination of cognitive adaptability, resilience under stress, and professional motivation (Smith et al., 2022). The MMI domains most strongly associated with KFQ performance – situational awareness, decision-making, crisis management, and motivation – represent a cluster of non-cognitive attributes that appear to co-occur with reasoning performance, underscoring the importance of including structured assessments of these competencies in residency selection.

In contrast, GPA and MCQ scores did not correlate with KFQ outcomes. While these measures reflect academic achievement and knowledge acquisition, they do not necessarily capture the applied reasoning and judgement required in patient care (Rencic et al., 2020). GPA may reflect long-term academic diligence, and MCQs assess factual recall and recognition, but neither provides insight into how knowledge is synthesised or applied in dynamic, uncertain contexts (Daniel et al., 2019). This explains why these traditional academic metrics, although useful, were weaker predictors of reasoning ability compared to MMI competencies. Similarly, MMI domains such as stress and mood management and growth mindset, though important for professional development and long-term adaptability, may exert more indirect effects on reasoning. Their influence may emerge later in residency training, when resilience and adaptability are tested by sustained clinical workload, rather than in reasoning-focused assessments like KFQs (Hrynchak et al., 2014).

Our findings are consistent with prior research demonstrating the predictive validity of MMIs for clinical performance and professionalism-related traits (Duff et al., 2020; Jerant et al., 2019; Kim et al., 2014). However, unlike studies showing strong associations between MMIs and academic achievement in dental and medical students (Jerant et al., 2019; Li et al., 2025; Partido & Soto, 2019), we did not observe such relationships. This discrepancy may be due to our focus on clinical reasoning, which is more closely tied to performance in practice than to traditional academic outcomes (Jerant et al., 2019; Kim et al., 2014). Clinical performance relies on situational awareness and judgement, whereas academic performance encompasses broader constructs defined by educational systems (Rencic et al., 2020; Zagury-Orly et al., 2022). While there is some overlap, reasoning does not fully account for academic achievement (Daniel et al., 2019; Smith et al., 2022; Zagury-Orly et al., 2022).  Prior work also suggests a bidirectional relationship, with academic support – such as encouragement to ask questions and access to literature – enhancing reasoning, while reasoning competencies like situational judgement and decision-making predict future performance (Li et al., 2025; Zagury-Orly et al., 2022). KFQs, by emphasising the application of knowledge to context-specific problems, complement the MMI by capturing reasoning skills in a structured format (Hrynchak et al., 2014; Naz et al., 2025). Together, these tools highlight the importance of evaluating both cognitive and non-cognitive domains in the selection process.

A. Implications for residency selection and training

These findings suggest that residency selection should extend beyond academic metrics to incorporate structured non-cognitive assessments. In particular, situational awareness and decision-making showed the strongest associations with reasoning performance, reinforcing their relevance in identifying candidates likely to excel in the complex, dynamic environment of anaesthesiology. The absence of significant associations for GPA and MCQ scores emphasises the limitation of relying primarily on academic performance in resident selection. A balanced model – integrating MMIs, cognitive tests, and KFQs – may provide a more comprehensive assessment of candidate suitability.

Residency training should also reinforce the competencies identified at selection. Encouraging questioning, providing access to literature, and promoting deliberate practice can strengthen reasoning processes and decision-making. Embedding these strategies within curricula may foster both cognitive and non-cognitive skills, thereby ensuring residents are prepared for safe and effective clinical practice.

B. Strengths and limitations

A key strength of this study is its focus on the relationship between non-cognitive competencies assessed by the MMI and clinical reasoning performance measured by KFQs, an area that has received limited attention in anaesthesia residency selection research. By examining both cognitive and non-cognitive predictors, the study provides a more comprehensive perspective on candidate evaluation, highlighting the distinct contribution of situational awareness and decision-making to clinical reasoning. The use of multiple data sources – including MMI competencies, GPA, MCQ scores, and KFQs – allowed for a robust comparison of predictors, strengthening the validity of the findings. Additionally, the inclusion of a full cohort across multiple admission cycles reduces sampling bias and enhances the reliability of the results.

However, several limitations should be acknowledged. First, the study was conducted within a single institution, which may limit the generalisability of the findings to other residency programmes with different educational cultures, curricula, or selection frameworks. Institution-specific factors – such as the structure of the anaesthesiology training programme, alignment between selection tools and curricular competencies, and the design of the MMI and KFQ assessments – may have influenced applicant performance and the observed relationships. Second, the retrospective design restricts the ability to establish causal relationships between MMI competencies and clinical reasoning performance. Third, formal cross-validation was not undertaken, as the analysis was based on a single, complete cohort without an external validation dataset; as such, the findings should be interpreted as exploratory rather than confirmatory. Future multi-institutional or prospective studies with independent validation samples are needed to assess the robustness and generalisability of these associations. In addition, although KFQs are a validated and practical proxy for assessing clinical reasoning at key decision points, they cannot fully capture the complexity of real-world clinical reasoning, which is influenced by contextual, environmental, and team-based factors such as workload, time pressure, and interprofessional interactions. Finally, the study focused on short-term performance outcomes; long-term follow-up linking selection measures to clinical performance during residency and independent practice would provide stronger evidence for predictive validity. Furthermore, formal psychometric properties of the MMI stations, including inter-rater agreement and generalisability coefficients, were not available for the current dataset; for the KFQ, inter-rater reliability was assessed and reported in the Methods section (r = 0.86). This is acknowledged as a limitation, and future studies should incorporate comprehensive psychometric evaluation for both instruments. Additionally, it was not possible to include sample MMI scenarios or KFQ items as supplementary material, as both item sets remain part of the active item banking pool currently used in the department’s ongoing annual residency admission process. Public disclosure of these items could compromise the integrity of future selection cycles; this constraint is acknowledged as a limitation of this report.

C. Future directions

Future research should employ multi-institutional and prospective designs to test generalisability and causality. Longitudinal studies linking MMI and KFQ results to workplace-based assessments, simulation performance, and patient care indicators would provide stronger evidence of predictive validity. Programmes could also track whether residents who score highly in domains such as situational awareness and decision-making during selection subsequently demonstrate superior clinical reasoning and performance in training. This would not only validate the predictive utility of MMIs but also inform ongoing curriculum design and personalised learning plans. Research should further explore how educational interventions – such as structured feedback, mentoring, and deliberate practice – enhance the competencies most strongly tied to reasoning. Incorporating complementary tools, such as entrustable professional activities (EPAs) and situational judgement tests, may enrich assessment frameworks and provide a more holistic evaluation. Ultimately, aligning residency selection and curricula with competencies most relevant to clinical reasoning can better prepare residents for the demands of practice and enhance patient care outcomes. 

V. CONCLUSION

This study found that key MMI domains – situational awareness and decision-making, crisis management, and motivation – were significantly associated with clinical reasoning performance as measured by KFQs among anaesthesia residency candidates, whereas GPA and MCQ scores were not. These findings underscore the value of incorporating structured non-cognitive assessments alongside cognitive measures in residency selection. However, as MMI and KFQ assessments were administered within the same selection cycle, these results reflect associations between constructs rather than predictive relationships. Further multi-institutional, prospective studies are required to determine predictive validity and long-term educational impact, and to confirm that candidates demonstrating these competencies at selection subsequently exhibit superior clinical reasoning during residency training.

 Notes on Contributors

Kasana Raksamani conceptualised and designed the study, supervised the research process, curated and analysed the data, and drafted and revised the manuscript.

Wathinee Phaknam contributed to the study conceptualisation and methodology, conducted investigation and data curation, and assisted in manuscript revision.

Sahatsa Mandee participated in study design and investigation, provided resources, conducted literature review and data curation, and contributed to manuscript revision.

Nuanprae Kitisin contributed to the study conceptualisation and investigation, performed data analysis and visualisation, and revised the manuscript.

Tachawan Jirativanont contributed to conceptualisation and methodology, validated the study, and assisted in manuscript revision.

All authors have read and approved the final manuscript and agree to be accountable for all aspects of the work.

Ethical Approval

This study was conducted in accordance with Siriraj Institutional Review Board (SIRB) protocols. Ethical approval was obtained from the SIRB, and the requirement for informed consent was waived as the study involved retrospective analysis of existing data with no identifiable personal information. The IRB approval reference number/waiver confirmation is [Si 496/2025].

Data Availability

The datasets generated and analysed during the current study are not publicly available due to restrictions imposed by the Institutional Review Board (IRB), which prohibit sharing of individual-level data. Aggregated or de-identified data may be made available from the corresponding author upon reasonable request and with IRB approval. 

Acknowledgement

The authors would like to express their sincere gratitude to Ms Pavinee Sareenun for her dedicated assistance with data collection, which was essential to the completion of this study. We also extend our appreciation to the Postgraduate Unit of the Department of Anesthesiology for their continuous administrative and logistical support. In addition, the authors acknowledge the use of a large language model (ChatGPT, OpenAI) to assist with English language editing. All content was reviewed and verified by the authors, who take full responsibility for the final manuscript.

Funding

This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of Interest

The authors declare no conflicts of interest related to this study.

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*Kasana Raksamani
Department of Anesthesiology,
Faculty of Medicine Siriraj Hospital,
Mahidol University, Thailand, 10700
Contact No: +66 967841010
Email: kasana.rak@mahidol.ac.th