Ten pressure points in primary care during COVID-19: findings from an international narrative review
Bibliographic record
Abstract
BACKGROUND: Strong primary care (PC) services are the foundation of high-performing health care systems and can support effective responses to public health emergencies. Primary care practitioners (PCPs) and PC services played crucial roles in supporting global health system responses to the COVID-19 pandemic. However, these contributions have come at a cost, impacting on PC services and affecting patient care. This secondary analysis of data from an integrative systematic review across international PC settings aimed to identify and describe burdens and challenges experienced by PCPs and PC services in the context of their contributions to COVID-19 pandemic responses. METHODS: We conducted an integrative systematic review and narrative analysis, searching PubMed/Medline, Scopus, Proquest Central and Cochrane Database of Systematic Reviews, plus reference lists of key publications. Included studies were published in peer-reviewed English or Chinese language journals, and described collective responses to COVID-19 undertaken in PC settings or by PCPs. Narrative data regarding impacts on PC services and challenges experienced by PCPs were extracted and analysed using inductive coding and thematic analysis. RESULTS: From 1745 screened papers 108, representing 90 countries, were included. Seventy-eight contained data on negative impacts, challenges or issues encountered in PC. Ten 'pressure points' affecting PC during COVID-19 were identified, clustered in four themes: demand to adopt new ways of working; pressure to respond to fluctuating community needs; strain on PC resources and systems; and ambiguity in interactions with the broader health and social care system. CONCLUSIONS: PCPs and PC services made critical functional contributions to health system responsiveness during the COVID-19 pandemic. However, both practitioners and PC settings were individually and collectively impacted during this period as a result of changing demands in the PC environment and the operational burden of additional requirements imposed on the sector, offering lessons for future pandemics. This study articulates ten empirically derived 'pressure points' that provide an initial understanding of burdens and demands imposed on the international primary care sector during the COVID-19 pandemic. The impact of these contributions should inform future pandemic planning, guided by involvement of PCPs in public health preparedness and policy design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".