Social Cohesion and Community Resilience During COVID-19 and Pandemics: A Rapid Scoping Review to Inform the United Nations Research Roadmap for COVID-19 Recovery
Bibliographic record
Abstract
Shock events uncover deficits in social cohesion and exacerbate existing social inequalities at the household, community, local, regional, and national levels. National and regional government recovery planning requires careful stakeholder engagement that centers on marginalized people, particularly women and marginalized community leaders. The aim of this rapid scoping review was to inform the United Nations Research Roadmap for the COVID-19 Recovery, based on Pillar 5 of the United Nations Framework for the Immediate Socioeconomic Response to COVID-19: Social Cohesion and Community Resilience. We present a summary of key concepts across the literature that helped situate this review. The results include a description of the state of the science and a review of themes identified as being crucial to sustainable and equitable recovery planning by the United Nations. The role of social cohesion during a disaster, particularly its importance for upstream planning and relationship building before a disaster occurs, is not well understood and is a promising area of future research. Understanding the applicability of social cohesion measurement methodologies and outcomes across different communities and geographies, as well as the development of new and relevant instruments and techniques, is urgently needed in the context of the global COVID-19 pandemic.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".