An Evaluation of Physician Retention Bonuses in Newfoundland and Labrador
Bibliographic record
Abstract
Context: Physician retention, particularly in rural areas, is a huge challenge facing the Canadian healthcare system. Regions both within Canada and globally have implemented financial incentives for physician retention but there is very little research examining their effectiveness. Objective: To evaluate the impact of retention bonuses on length of time physicians worked in a community and community level turnover. Study Design and Analysis: Descriptive, ARIMA analysis. Setting or Dataset: Data from the Medical Practice Registry from the Newfoundland and Labrador Department of Health were used. Population Studied: All physicians who worked in Newfoundland and Labrador (NL) between 2000 and 2015, excluding locums, residents, administrators, and those in full time teaching roles. Intervention: NL implemented the Rural Fee for Service Retention Bonus program for salaried physicians in 2003 and for all physicians in 2009. The retention bonuses are divided into four tiered categories based on geographic location. Category 0 and Category 1 communities have the highest need and physicians receive the highest bonuses, and Category 3 communities have the lowest need and receive the lowest bonuses. Outcome Measures: We measured two outcomes: physician-level retention, and community-level retention. Results: 1707 physicians who worked in NL between 2000 and 2015 were included in the study. The percentage of the workforce made up of graduates from Memorial University increased throughout the study period. Our analyses found that the percentage of International Medical Graduates (IMGs) working as family physicians in rural communities increased from 32.8% to over 60% throughout the study period, indicating that the rural physician workforce relies heavily on IMGs. We conducted ARIMA analyses to assess the effectiveness of the retention bonus programs and found no significant impact of the 2003 retention bonus. We found significant impacts of the 2009 retention bonus; a small increase in physician-level retention for specialists in Category 2 communities and a decrease in Category 3 communities. Conclusions: Implementing retention bonuses in NL did not improve physician- or community-level retention. Study findings suggest that resources could be better invested in other supports to increase physician retention. The results of our study have implications for other jurisdictions in Canada facing similar physician workforce challenges.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".