Strengthening the integration of primary care in pandemic response plans: a qualitative interview study of Canadian family physicians
Bibliographic record
Abstract
BACKGROUND: As the first point of contact in health care, primary care providers play an integral role in pandemic response. Despite this, primary care has been overlooked in previous pandemic plans, with a lack of emphasis on ways in which the unique characteristics of family practice could be leveraged to create a more effective response. AIM: To explore family physicians' perceptions of the integration of primary care in the COVID-19 pandemic response. DESIGN AND SETTING: Descriptive qualitative approach examining family physician roles during the COVID-19 pandemic across four regions in Canada. METHOD: Semi-structured qualitative interviews were conducted with family physicians and participants were asked about their roles during each pandemic stage, as well as facilitators and barriers they experienced in performing these roles. Interviews were transcribed and a thematic analysis approach was employed to develop a unified coding template across the four regions and identify recurring themes. RESULTS: In total, 68 family physicians completed interviews. Four priorities for integrating primary care in future pandemic planning were identified: 1) improve communication with family physicians; 2) prioritise community-based primary care; 3) leverage the longitudinal relationship between patients and family physicians; and 4) preserve primary care workforce capacity. Across all regions, family physicians felt that primary care was not well incorporated into the COVID-19 pandemic response. CONCLUSION: Future pandemic plans require greater integration of primary care to ensure the delivery of an effective and coordinated pandemic response. Strengthening pandemic preparedness requires a broader reconsideration and better understanding of the central role of primary care in health system functioning.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".