Adverse childhood experiences and stress among oral health students: a descriptive correlational study.
Bibliographic record
Abstract
Background: Stress is a challenge to many post-secondary students and, if prolonged and unmanaged, can affect academic success. Understanding factors that contribute to students' stress is important. One possible contributor may be adverse childhood experiences (ACEs); that is, traumatic events that occur during the first 18 years of life. Inverse relationships between the number of ACEs and indicators of poor mental well-being have been proposed. Objective: To describe ACEs in oral health students (OHS) and the associations between the number and types of ACEs and levels of perceived stress, an indicator of mental well-being. Methods: Invitations to participate in an anonymous online cross-sectional survey were sent to all OHS, 19 years and older, attending Dalhousie University in Halifax, Nova Scotia, Canada. Self-reports of ACEs and perceived stress were collected. Zero-order correlations and regression modelling were used to examine associations. Results: = 0.092). Discussion: This was the first study to examine associations between ACEs and perceived stress in OHS. These students reported greater numbers of ACEs than age-matched general populations. Levels of stress were associated with numbers of ACEs. Conclusion: Faculty in dental and dental hygiene programs should recognize the prevalence of ACEs among OHS and the potential impact on their mental 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".