An integrative longitudinal resilience curriculum
Bibliographic record
Abstract
BACKGROUND: It is well documented that student well-being is challenged at medical school and that levels of distress increase as students navigate their training. The Doctor of Medicine (MD) programme at the University of Toronto developed a 4-year resilience curriculum (RC) to encourage students to reach out for help and equip them with resilience-building strategies to manage adversities in a demanding academic and clinical programme. … resilience curriculum (RC) to encourage students to reach out for help and equip them with resilience-building strategies METHODS: Satisfaction surveys, consisting of statements rated by a five-point Likert scale and short-answer questions, were distributed to 518 students; in total, data from four workshops were collected. Two focus groups comprising 12 participants in total were facilitated (n = 6 per group). A thematic content analysis was conducted for the focus group data; open coding was used for transcriptions via an iterative process and inductive analysis. FINDINGS: Preliminary quantitative and qualitative data suggest that students valued the curriculum. The main themes generated from the thematic content analysis were the value of the RC, the delivery of the RC, and developing a resilient community. DISCUSSION: More research must be conducted to assess whether the RC has affected student well-being and resilience. The sustainability of the curriculum depends on the faculty members that support it; faculty development within the areas of wellness and resilience is imperative. INNOVATION AND IMPLICATIONS: The RC, embedded in the core curriculum and integrated within a medical community, is gaining momentum and is valued by students. Further research will assist in the creation of an innovative tool to assess the impact of the RC on medical students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".