Professionalism in Residency Training: The Learning Environment for Professionalism Survey
Bibliographic record
Abstract
Role modelling is important in developing professionalism with a need for reliable, evidence-based tools to assess professionalism in the learning environment (LE). The Learning Environment for Professionalism (LEP) survey is brief, anonymous and balanced assessing medical trainees' and attendings’ positive and negative professionalism behaviours that can be tracked longitudinally and identify problem areas in the LE. Seven training programs agreed to facilitate administration of the LEP survey at four hospitals in Ottawa, Canada. The survey was carried out iteratively between 2013 and 2020. A total of 3783 LE ratings of training programs and hospitals were assessed longitudinally using univariate linear regression. A Bonferroni corrected p -value of ≤.0045 was used to account for multiple comparisons. Positive professional behaviours were observed across time with some of the negative behaviors having improved. A negative signal was found, with attendings appearing to be treating patients unfairly because of their financial status, ethnic background, sexual or religious preferences. Applying LEP survey longitudinally across diverse training programs and institutions is feasible and may assist programs to identify areas requiring attention and acknowledging areas of exemplary professionalism. Continuous monitoring of LE to meet requirements of accrediting bodies can also be considered an important quality improvement metric.
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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 | 0.005 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".