Learning environment: assessing resident experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it