Transformational learning and professional identity formation in postgraduate competency‐based medical education
Bibliographic record
Abstract
INTRODUCTION: Residency programmes are in transition to a framework for competency-based medical education (CBME). The intersection of CBME with transformational learning (TL) experiences and professional identity formation (PIF) - particularly within senior learners in transitional states - is unknown but important to understand in order to develop and implement strategies to support trainees' professional development. METHODS: Through inductive qualitative methods, we conducted semi-structured interviews (n = 22) of current trainees and recent graduates from adult cardiology residency training programmes within Canada to explore the impact of TL experiences on residents' professional growth and identity formation. Interviews were analysed using thematic analysis informed by TL theory. RESULTS: CBME did not appear to influence trainees' experiences of disorienting dilemmas and TL. Important clinical encounters and interpersonal relationships - in particular, those between mentor and mentee - shaped trainees' professional development as cardiologists ('enabling factors' for TL and PIF). 'Imposter phenomenon' was widely prevalent in our sample study population even among graduates who had already completed their training. Requisite elements for transformation (disorienting dilemmas, critical reflection, discourse and action) also contributed to PIF. DISCUSSION: TL experiences influenced PIF in senior learners but infrequently intersected with CBME; these experiences were more commonly prompted by disorienting dilemmas relating to clinical outcomes or interpersonal interactions independent of CBME-specific architecture.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".