What works and why in interventions to strengthen social cohesion: A systematic review
Bibliographic record
Abstract
Abstract COVID‐19 has highlighted worldwide the importance of a strong social and political fabric. Those countries that fared best were ones where there was community connection, belonging, a volunteering ethos, and a belief in the legitimacy of official institutions, all deemed critical aspects of social cohesion. It has become clear that understanding and strengthening social cohesion in times of stability is critical to successfully navigate crisis. Despite its importance, evidence from many countries indicates that this important “social glue” is fragile and at risk, requiring consistent investments to maintain and strengthen it. Governments and communities around the world are looking to evidence‐based strategies to strengthen social cohesion. To facilitate this goal, a systematic review is conducted of four major databases identifying 52 studies with high‐quality evidence of what works and why. We also included the results of three systematic reviews that had investigated the impact of social capital and/or social cohesion on health‐related variables specifically to broaden our search and enrich our findings ( n = 21; total = 73). Using themes identified across governments, it is possible to classify the strengths and limitations of existing research. It becomes clear that the most common effective strategies were (1) awareness raising and coutering existing stereotypes and (2) offering opportunities for positive contact and a more co‐operative assessment of intergroup relations. Missing are leadership processes that can (re)define group‐based values, norms, and behaviors. Specific intervention strategies are outlined as well as directions for future research.
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 | 0.038 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".