Lessons for the global primary care response to COVID-19: a rapid review of evidence from past epidemics
Bibliographic record
Abstract
BACKGROUND: COVID-19 is the fifth and most significant infectious disease epidemic this century. Primary health care providers, which include those working in primary care and public health roles, have critical responsibilities in the management of health emergencies. OBJECTIVE: To synthesize accounts of primary care lessons learnt from past epidemics and their relevance to COVID-19. METHODS: We conducted a review of lessons learnt from previous infectious disease epidemics for primary care, and their relevance to COVID-19. We searched PubMed/MEDLINE, PROQUEST and Google Scholar, hand-searched reference lists of included studies, and included research identified through professional contacts. RESULTS: Of 173 publications identified, 31 publications describing experiences of four epidemics in 11 countries were included. Synthesis of findings identified six key lessons: (i) improve collaboration, communication and integration between public health and primary care; (ii) strengthen the primary health care system; (iii) provide consistent, coordinated and reliable information emanating from a trusted source; (iv) define the role of primary care during pandemics; (v) protect the primary care workforce and the community and (vi) evaluate the effectiveness of interventions. CONCLUSIONS: Evidence highlights distinct challenges to integrating and supporting primary care in response to infectious disease epidemics that have persisted over time, emerging again during COVID-19. These insights provide an opportunity for strengthening, and improved preparedness, that cannot be ignored in a world where the frequency, virility and global reach of infectious disease outbreaks are increasing. It is not too soon to plan for the next pandemic, which may already be on the horizon.
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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 | 0.036 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".