Social capital and public health: responding to the COVID-19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it