Students' resilience and mental health in the dental curriculum
Bibliographic record
Abstract
OBJECTIVES: Dental education is perceived as a source of students' psychological and occupational stress. Resilience has been proposed as a protective factor that may support students' in managing that stress. The objectives of this study were twofold: to map the mental health and well-being content in the curriculum of the Faculty of Dentistry (FoD) at the University of British Columbia (UBC) and to investigate factors influencing resilience levels amongst dental students at UBC. METHODS: The curricular database and website of UBC's FoD were used to gather information on mental health content. A survey with the Connor-Davidson 10-Item Resilience Scale was distributed to dental students at UBC (N = 289). Students' de-identified demographic data were also collected. RESULTS: Two main mental health and well-being curricular components were identified: one didactic session on stress management and one interactive workshop on resilience. The response rate for the survey was 68.2%. Students who did not receive any mental health content (2020/21 year 1 students) had higher resilience scores (p = .043) when compared to students who received both components (2019/20 year 1 students and 2018/19 year 2 students). The multiple regression analysis highlighted North American/European ethnic origins as a predictor for higher resilience levels (p = .008). CONCLUSIONS: The results of this study showed that ethnic origins and major life events, such as the pandemic, influenced resilience. Curricular activities promoting resilience seemed to not necessarily impact students' resilience. Further longitudinal studies are needed to assess the curricular and non-curricular activities influence over dental students' well-being.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".