Understanding the Mental Health of Canadian Physician Assistant Students
Bibliographic record
Abstract
The mental health of medical students has been well studied, revealing significant rates of anxiety, depression, and burnout. Despite the similarities in training, limited research has been done to understand the mental health of Physician Assistant (PA) students. This study investigates the mental health of first- and second-year PA students across Canadian training programs. Eligible students at the three Canadian PA programs received a recruitment email providing a link to a confidential survey hosted on REDCap. Participants provided non-identifying demographic information and completed the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Screener (GAD-7), and the Maslach Burnout Inventory General Student version (MBI-GS(S)). Additional questions covered previous mental health history and coping styles. Seventy-three students completed the survey (response rate of 43.4%). One-third had seen a health professional in the past 12 months for mental health. Depression, anxiety, and burnout symptoms were prevalent. GAD-7 varied significantly between schools (higher at University of Manitoba vs McMaster University), and marginally between academic year (higher in first years). Positive correlations were found among PHQ-9, GAD-7, and burnout subscales. Maladaptive coping mechanisms were associated with higher anxiety, depression, and burnout, while adaptive coping positively correlated with PE burnout subscale. No significant differences in PHQ-9 or MBI-GS(S) were found between academic years or programs. As the PA profession continues to expand in Canada, understanding the prevalence of anxiety, depression and burnout is crucial. Improving PA student mental health should be a priority for education programs to promote a healthy workforce and develop a foundation for further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".