An Evaluation of the Physician Retention Program in Newfoundland and Labrador
Bibliographic record
Abstract
Did physician retention bonuses improve retention in Newfoundland and Labrador (NL)?\nData from the Medical Practice Registry from the Newfoundland and Labrador Department of Health and the Canadian Institute for Health Information (CIHI) were analyzed to 1) describe the physicians who work in NL, 2) examine physician length of practice (retention) in a community, and 3) examine community level retention.\n1701 physicians who worked in NL between 2000 and 2015 were included in the study. 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 a significant increase in physician retention for specialists in Category 2 communities and a decrease in Category 3 communities following the 2009 retention bonus.\nRetention 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.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".