Investigating Physician Assistant burnout amidst the COVID-19 global pandemic: a qualitative survey response from practicing PAs in Canada
Bibliographic record
Abstract
This purpose of this study was to determine if there is an underlying element of burnout among practicing Physician Assistants (PAs) across Canada during the global COVID-19 pandemic and uncover any potential solutions for this arduous problem. A survey encompassing the Maslach Burnout Inventory (MBI)and qualitative questions was emailed to practicing Canadian PAs. A total of 118 practicing PAs fully completed the survey; the majority reported high levels of burnout, specifically on depersonalization and emotional exhaustion subscales, while all maintained a high level of personal accomplishment simultaneously. The majority of respondent PAs listed increased staffing, increased time off/consistent scheduling, and pandemic pay among others as major solutions to alleviate burnout in the future. In conclusion, Canadian PAs working during the global pandemic are indeed experiencing burnout, all while displaying a high level of resilience in certain MBI subscales. The individual responses provided by these frontline workers may highlight critical solutions that may be generalized to other healthcare jobs in order to prevent future occupational burnout.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".