Learning environment: assessing resident experience
Bibliographic record
Abstract
BACKGROUND: Given their essential role in developing professional identity, academic institutions now require formal assessment of the learning environment (LE). We describe the experience of introducing a novel and practical tool in postgraduate programmes. The Learning Environment for Professionalism (LEP) survey, validated in the undergraduate setting, is relatively short, with 11 questions balanced for positive and negative professionalism behaviours. LEP is anonymous and focused on rotation setting, not an individual, and can be used on an iterative basis. We describe how we implemented the LEP, preliminary results, challenges encountered and suggestions for future application. Academic institutions now require formal assessment of the learning environment METHODS: The study was designed to test the feasibility of introducing the LEP in the postgraduate setting, and to establish the validity and the reliability of the survey. Residents in four programmes completed 187 ratings using LEP at the end of one of 11 rotations. RESULTS: The resident response rate was 87 per cent. Programme and rotation ratings were similar but not identical. All items rated positively (favourably), but displays of altruism tended to have lower ratings (meaning less desirable behaviour was witnessed), as were ratings for derogatory comments (again meaning that less desirable behaviour was witnessed). DISCUSSION: We have shown that the LEP is a feasible and valid tool that can be implemented on an iterative basis to examine the LE. Two LEP questions in particular, regarding derogatory remarks and demonstrating altruism, recorded the lowest scores, and these areas deserve attention at our institution. Implementation in diverse programmes is planned at our teaching hospitals to further assess reliability. This work may influence other postgraduate programmes to introduce this assessment tool.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".