Mental health and wellness in Canadian dental schools: Findings from a national study
Bibliographic record
Abstract
OBJECTIVES: To survey the mental health and wellbeing content in the curricula, services, and activities of the 10 Canadian dental schools, and to explore the specifics of this area in the Faculty of Dentistry (FoD) at The University of British Columbia (UBC). METHODS: An electronic survey consisted of four major categories: curricular activities and services, structural approaches, infrastructural approaches, and evaluation methods, was distributed to all Canadian dental schools. A situational analysis was conducted at UBC's FoD via document appraisal and key informants' exploratory interviews. RESULTS: Eight dental schools responded to the survey showing that didactic sessions being the pedagogical method to deliver resilience content. None of the responding schools reported formally evaluating their mental health content. Through situational analysis, a relational map that identified four major areas contributing to students' mental health at UBC's FoD was generated which includes four major aspects: (1) curricular content on mental health, (2) informal wellbeing and mental health networks, (3) protective, and (4) risk factors influencing students' mental health. CONCLUSIONS: As this study described the mental health and wellbeing activities, services, and curricular content across multiple Canadian dental schools, the diverse approaches each school adopted and how personal and professional aspects of students' lives being attempted to be addressed are a critical starting point to engage educators in dentistry. The situational analysis outcome, where a detailed description of the mental health situation at UBC's FoD, can be used to guide in-depth studies of the area of wellbeing at other dental schools.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".