Before the white coat: perceptions of professional lapses in the pre‐clerkship
Bibliographic record
Abstract
BACKGROUND: It has been shown that the professional development of clinical clerks is influenced by their experiences of unprofessional behaviour, but the perceptions of pre-clerkship students have received relatively little attention. Our purpose was to develop a greater contextual understanding of the situations in which pre-clerkship students encounter professional challenges, and to investigate what pre-clerkship students consider to be professional lapses in these situations. METHODS: We conducted 4 focus groups (n = 22 students); transcripts were analysed by 3 researchers using grounded theory. RESULTS: Pre-clerkship students reported lapses in the areas of communicative violation, role resistance, objectification, accountability and harm, validating our previous clerkship-based framework. However, they also reported numerous lapses committed by fellow students and many instances of lack of accountability to students, which were not reported by clerks. Many of their reports involved non-health care professionals. CONCLUSIONS: The willingness of pre-clerkship students to report on fellow students was associated with a tendency to blame their colleagues, at the expense of a more reflective analysis, and their views on professionalism appeared to be generic rather than medicine-specific. We should reinforce students' appreciation of these generic values and add on medicine-specific values as the students progress, in order to better cultivate professionalism without entitlement.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".