How do medical students define a Health Promoting Learning Environments?
Bibliographic record
Abstract
Background: Medical students in Canada report significantly higher rates of suicidal ideation, psychological stress, as well as mood and anxiety disorders, compared to age matched general population. We are still early in the process of having a comprehensive approach to learner wellbeing that centers around health promoting learning environments (HPLE) - focusing on more systemic actions as guided by the international Okanagan Charter. To move forward, we need to further understand what learners, faculty and staff view as critical components in an HPLE and explore how we can best advance efforts to create and embed HPLEs in medical education. The objectives of this study were to elucidate how medical students define an HPLE and what medical students perceive as the main barriers and facilitating factors to developing and fostering HPLEs. Methods: We undertook an exploratory qualitative study using virtual semi-structured interviews of how medical students define an HPLE and the facilitators and barriers of this. We used thematic analysis to review all transcripts with ongoing iterative analysis. Final themes were agreed on consensus. Results: We interviewed 14 medical students from all years at the University of Alberta. We identified four overarching themes which serve as important components of an HPLE including that HPLEs have foundational characteristics of respect, transparency, and open communication. Developing HPLEs require multi-pronged approaches that starts with ensuring basic needs are met and empowering learners to make health promoting choices. Learners identified that a culture of wellbeing is driven by wellbeing centered leadership. A safe space to take an active role in influencing their environment help learners thrive. Conclusions: Our study focused on elucidating medical student perspectives on factors that contribute to and foster a health promoting learning environment. Our findings can inform on systemic efforts to embed wellbeing into medical education in Canada.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".