Social capital and public health: responding to the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: As countries continue to respond to the COVID-19 pandemic, the importance of ensuring that fair and equal access to healthcare for all is more urgent than ever. Policies that promote social capital building along all levels of society may offer an important avenue for improved healthcare delivery and health systems strengthening in the COVID-19 response. MAIN BODY: In reference to the established and emerging literature on social capital and health, we explore the role of social capital in the COVID-19 health policy response. We analyse current research with respect to mental health, public health policy compliance, and the provision of care for vulnerable populations, and highlight how considerations of bonding, bridging, and linking capital can contribute to health systems strengthening in the context of the COVID-19 response and recovery effort. CONCLUSIONS: This article argues that considerations of social capital - including virtual community building, fostering solidarity between high-risk and low-risk groups, and trust building between decision-makers, healthcare workers, and the public - offer a powerful frame of reference for understanding how response and recovery programs can be best implemented to effectively ensure the inclusive provision of COVID-19 health services.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.024 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".