Professionalism lapses in health professions training: Navigating the ‘Yellow Card’ moments for transformative learning
Bibliographic record
Abstract
INTRODUCTION: Health professions training programmes face increasing reports of professionalism lapses, which can delay, or end, trainee progression. How programmes respond to professionalism lapses to facilitate professional identity development has not been clarified. The objective of this study is to identify factors that facilitate and impair transformations around professionalism lapses in health professions training programmes. METHODS: We conducted a qualitative study interviewing 5 faculty and 20 trainees with firsthand or secondhand experience with professionalism lapses from a range of health professions training programmes at McMaster University. Using reflexive thematic analysis, we coded verbatim transcripts informed by the lenses of social and transformative learning theories. We constructed themes through iterative and comparative analysis, seeking meaningful variation across professions and triangulating faculty and trainee perspectives. RESULTS: Four themes were constructed. First, lapses are in the eye of the beholder with personal definitions intersecting with institutional and situation norms. Difficulties exist in recognising and convincing trainees to respond to lapses that are perceived to be minor or subject to interpretation. Second, responses to professionalism lapses occurred within power hierarchies, which impacted how trainees reacted to the remediation process, risked superficial trainee responses to concerns and led to concerns around inequitable treatment in how standards were applied. Third, fostering transformation involves building trainee confidence, agency, trust and engagement. Focused support and advocacy for trainees can empower and promote agency in tackling disorienting lapses. Fourth, perspective shifts involve deep engagement over time, including but not limited to self-reflection, structured discussion and seeking support. DISCUSSION: Identifying and addressing professionalism lapses is complex and requires nuanced and contextual exploration of personal, institutional and situational dynamics at play. By fostering environments that promote genuine reflection and dialogue and focus on building trainee confidence, agency, trust and engagement, health professions training programmes can better support trainees in navigating these complex situations and contribute to the broader goal of socialising to a professional culture and practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".