An analysis of policies supporting the roles of family physicians in four regions in Canada during the COVID-19 pandemic
Bibliographic record
Abstract
Policy supports are needed to ensure that Family Physicians (FPs) can carry out pandemic-related roles. We conducted a document analysis in four regions in Canada to identify regulation, expenditure, and public ownership policies during the COVID-19 pandemic to support FP pandemic roles. Policies supported FP roles in five areas: FP leadership, Infection Prevention and Control (IPAC), provision of primary care services, COVID-19 vaccination, and redeployment. Public ownership polices were used to operate assessment, testing and vaccination, and influenza-like illness clinics and facilitate access to personal protective equipment. Expenditure policies were used to remunerate FPs for virtual care and carrying out COVID-19-related tasks. Regulatory policies were region-specific and used to enact and facilitate virtual care, build surge capacity, and enforce IPAC requirements. By matching FP roles to policy supports, the findings highlight different policy approaches for FPs in carrying out pandemic roles and will help to inform future pandemic preparedness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".