On the importance of primary and community healthcare in relation to global health and environmental threats: lessons from the COVID-19 crisis
Bibliographic record
Abstract
In the course of the COVID-19 pandemic, it has become clear that primary healthcare systems play a critical role in clinical care, such as patient screening, triage, physical and psychological support and also in promoting good community advice and awareness in coordination with secondary healthcare and preventive care. Because of the role of social and environmental factors in COVID-19 transmission and burden of disease, it is essential to ensure that there is adequate coordination of population-based health services and public health interventions. The COVID-19 pandemic has shown the primary and community healthcare (P&CHC) system's weaknesses worldwide. In many instances, P&CHC played only a minor role, the emphasis being on hospital and intensive care beds. This was compounded by political failures, in supporting local community resilience. Placing community building, social cohesion and resilience at the forefront of dealing with the COVID-19 crisis can help align solutions that provide a vision of 'planetary health'. This can be achieved by involving local well-being and participation in the face of any pervasive health and environmental crisis, including other epidemics and large-scale ecological crises. This paper proposes that P&CHC should take on two critical roles: first, to support local problem-solving efforts and to serve as a partner in innovative approaches to safeguarding community well-being; and second, to understand the local environment and health risks in the context of the global health perspective. We see this as an opportunity of immediate value and broad consequence beyond the control of the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.015 |
| 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".