Factors That Influence Physician Assistant Job Satisfaction and Retention in Manitoba: A Survey Response
Bibliographic record
Abstract
Introduction: A significant amount of healthcare providers in Canada are suffering from job dissatisfaction, burnout, or plan to leave healthcare in the next few years. Physician Assistants (PA) were the fastest-growing sector of this Canadian healthcare workforce in 2021. Despite this, very little research has been conducted to date on the factors that influence PA job satisfaction and retention in Canada. Research from the United States has shown that autonomy, compensation, physician support, and role flexibility are highly associated with job satisfaction among PAs. Objective: The objective of this study was to undertake a census of working PAs in Manitoba and to determine what employment conditions were considered important to PAs and may influence job satisfaction and retention. Methods: An online survey was constructed and distributed to 153 registered and licensed PAs in the province of Manitoba. The survey consisted of a demographic collection section, a quantitative assessment of relevant employment conditions on a 5-point Likert scale, and a qualitative section with open questions analyzed via keyword analysis. Results: Employment conditions found to be the most important were schedule, hours worked per week, amount of vacation, physician/supervisor support, work culture or co-workers, and type of contract. Conditions considered important were compensation level, autonomy, receiving benefits/pension, practice area/specialty, type of employer, location of work, career advancement, and scope of practice. The most frequently encountered keywords were supervisor, flexibility, contract, and schedule. Conclusion: This study demonstrated that autonomy and physician support continue to be key factors that drive PA retention and job satisfaction as seen in previous literature. However, greater attention should be given to job flexibility, scheduling, hours worked, and time away from work as they are of significant importance to PAs practicing in Manitoba and could reflect a broader professional trend in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".