Evaluating a novel resident role‐modelling programme
Bibliographic record
Abstract
BACKGROUND: Role modelling is a fundamental method by which students learn from residents. To our knowledge, however, resident-as-teacher curricula have not explicitly addressed resident role modelling. The purpose of this project was to design, implement and evaluate an innovative programme to teach residents about role modelling. METHODS: The authors designed a resident role-modelling programme and incorporated it into the 2015 and 2016 McGill University resident-as-teacher curriculum. Influenced by experiential and social learning theories, the programme incorporated flipped-classroom and simulation approaches to teach residents to be aware and deliberate role models. Outcomes were assessed through a pre- and immediate post-programme questionnaire evaluating reaction and learning, a delayed post-programme questionnaire evaluating learning, and a retrospective pre-post questionnaire (1 month following the programme) evaluating self-reported behaviour changes. RESULTS: Thirty-three of 38 (87%) residents who participated in the programme completed the evaluation, with 25 residents (66%) completing all questionnaires. Participants rated the programme highly on a five-point Likert scale (where 1 = not helpful and 5 = very helpful; mean score, M = 4.57; standard deviation, SD = 0.50), and showed significant improvement in their perceptions of their importance as role models and their knowledge of deliberate role modelling. Residents also reported an increased use of deliberate role-modelling strategies 1 month after completing the programme. Resident-as-teacher curricula have not explicitly addressed resident role modelling DISCUSSION: The incorporation of resident role modelling into our resident-as-teacher curriculum positively influenced the participants' perceptions of their role-modelling abilities. This programme responds to a gap in resident training and has the potential to guide further programme development in this important and often overlooked area.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".