Pandemic reflection: Significant change is needed to tap into the power of primary care during times of crisis
Bibliographic record
Abstract
Context: As a front-line resource, primary care could function as an indispensable health system resource during pandemic crises. However, throughout COVID-19, Canada's primary care providers expressed concern over their ability to respond. This study investigates factors related to these concerns, suggesting key areas for future primary care system development. Objective: Assess specific areas where robust, well-supported primary care could contribute significantly to the health system's pandemic response, & factors that may have prevented it from doing so. Study Design: Online, self-report survey administered over 3 weeks, April-May, 2021. Survey invitations sent via email, with three reminders following initial contact. Setting or Dataset: The College of Family Physicians of Canada (CFPC) membership list was used to reach family physicians in diverse practice settings in all Canadian provinces and territories. Population studied: All active CFPC family physician (FP) members were included. Most FPs in Canada are CFPC members; 39,991 FPs received survey invitations; 3,409 replied, for an overall response rate of 9%. Outcome Measures: Percent of FP engaged in systems level, pandemic response measures, including: 1) reporting COVID-19 cases to public health authorities, 2) identifying priority patients for vaccination, 3) participating in COVID-19 task forces, & 4) contributing to COVID-19 research/clinical studies. Results: Overall, one-third (34%) of FPs say that family practices in their region report COVID-19 cases to public health authorities. Most FPs (55%) say that their practices have not been asked to identify priority cases for COVID-19 vaccination. Less than 1-in-5 (19%) FPs have been involved in COVID-19 advisory/planning committees, task forces or groups. Very few FPs (6%) contribute to COVID-19 research/clinical studies. Half of FPs (50%) are highly concerned about the lack of clarity from government regarding FP response to the pandemic. These findings vary across jurisdictions and practice/remuneration models. Conclusions: Canada's primary care system has not been adequately engaged in important COVID-19 response measures, including monitoring viral spread in the population, pandemic planning, vaccination roll out, and therapeutic research. Practice models and remuneration arrangements are related to primary care's responsiveness to the COVID-19 pandemic,& should be considered in future primary care health system development.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".