Strategies to overcome physician shortages in northern Ontario: A study of policy implementation over 35 years
Bibliographic record
Abstract
BACKGROUND: Shortages and maldistribution of physicians in northern Ontario, Canada, have been a long-standing issue. This study seeks to document, in a chronological manner, the introduction of programmes intended to help solve the problem by the provincial government over a 35-year period and to examine several aspects of policy implementation, using these programmes as a case study. METHODS: A programme analysis approach was adopted to examine each of a broad range of programmes to determine its year of introduction, strategic category, complexity, time frame, and expected outcome. A chronology of programme initiation was constructed, on the basis of which an analysis was done to examine changes in strategies used by the provincial government from 1969 to 2004. RESULTS: Many programmes were introduced during the study period, which could be grouped into nine strategic categories. The range of policy instruments used became broader in later years. But conspicuous by their absence were programmes of a directive nature. Programmes introduced in more recent years tended to be more complex and were more likely to have a longer time perspective and pay more attention to physician retention. The study also discusses the choice of policy instruments and use of multiple strategies. CONCLUSION: The findings suggest that an examination of a policy is incomplete if implementation has not been taken into consideration. The study has revealed a process of trial-and-error experimentation and an accumulation of past experience. The study sheds light on the intricate relationships between policy, policy implementation and use of policy instruments and programmes.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".