Tackling Stress Management, Addiction, and Suicide Prevention in a Predoctoral Dental Curriculum
Bibliographic record
Abstract
Health care professionals, particularly dentists, are subject to high levels of stress. Without proper stress management, problems related to mental health and addiction and, to a lesser extent, deliberate self-harm such as suicide may arise. There is a lack of information on teaching methodologies employed to discuss stress management and suicide prevention in dental education. The purpose of this article is to describe a University of British Columbia Faculty of Dentistry module designed to address stress management and suicide prevention, using students' personal reflections to illustrate the impact of the pedagogies used. The module enrolls more than 200 students per year and has sessions tailored to the discussion of stress management and suicide prevention. The pedagogies include standardized patients, invited guest lectures, in-class activities, video presentation, and self-reflections. More than 500 students' self-reflections collected over the past five years illustrate the seriousness of the issues discussed and the level of discomfort students experience when pondering such issues. The instructors hope to have increased students' awareness of the stressors in their profession. Further studies are needed to unravel the extent to which such pedagogy influences a balanced practice of dentistry.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".