How do medical educators discern, decode, and act upon trainees appearing to engage in impression management?
Bibliographic record
Abstract
INTRODUCTION: Trainees are motivated to impress clinical teachers, making impression management common. Such efforts are beneficial, but problematic when 'targets' are misled or learning opportunities missed. Guiding faculty regarding impression management is difficult because little is known about what cues yield perceptions of being managed. METHODS: A qualitative study was performed to explore faculty perspectives on: (1) recognising cues indicative of impression management; (2) interpreting cues; and (3) using them. Fifteen educators from various specialities and cultural regions of Switzerland were interviewed. Transcripts were analysed thematically following grounded theory. They were reviewed by multiple reviewers with constant comparison undertaken. RESULTS: Faculty assumed trainees 'acted' whenever stakes were high. However, decoding impression management was deemed highly complex, mentally taxing, and fraught with uncertainty. Consideration of context was deemed important. Reactions to impression management were dynamic, ranging from benevolence to frustration, depending on perceptions of trainee motivations. DISCUSSION: Findings suggest trainees should be cautious about strategies used to influence preceptors. Impression management is accepted when considered an effort to improve capacity to act as a physician; it elicits negativity when trainees seem to be endearing themselves. Valuable leads are offered that should help educators and trainees remain engaged in effective educational alliances.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".