How community organisations impact health and wellbeing. A Common Health Assets (CHA) toolkit
Bibliographic record
Abstract
Background: The Common Health Assets project, funded by the NIHR and supported by Queen’s University Innovation Zones, explored how Community Led Organisations (CLOs) improve health and wellbeing in disadvantaged areas. These community-run groups play a crucial role in addressing health inequalities but often face precarious funding and inconsistent policy support. Methods: Working with 14 CLOs across the UK, researchers used a mixed methods realist evaluation of interviews, workshops, surveys, and financial analysis, to identify “programme theories” explaining how CLOs operate and improve wellbeing. Results: Community members participation in CLO activities led to significant gains in social connectedness, mental wellbeing, and quality of life. Early improvements appeared within one month, with sustained benefits at six to twelve months. Activities such as arts, education, and outdoor engagement each contributed differently to wellbeing. CLOs also reduced reliance on frontline health and housing services. Conclusions: CLOs are vital social infrastructure that foster trust, resilience, and inclusion. To sustain their impact, policy should prioritise multi-year core funding, structured volunteer support, and long-term investment in social prescribing frameworks. These measures would protect essential community health services, strengthen preventive care, and enable CLOs to deliver enduring benefits across generations. Without secure funding and recognition, communities risk losing a proven, evidence-based mechanism for improving health and wellbeing.
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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 | 0.034 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".