Geographic inequalities in need and provision of social prescribing link workers a retrospective study in primary care
Bibliographic record
Abstract
BACKGROUND: Long-term health conditions are major challenges for care systems. Social prescribing link workers have been introduced via primary care networks (PCNs) across England since 2019 to address the wider determinants of health by connecting individuals to activities, groups, or services within their local community. AIM: To assess whether the rollout of social prescribing link workers was in areas with the highest need. DESIGN AND SETTING: A retrospective study of social prescribing link workers in England from 2019 to 2023. METHOD: Workforce, population, survey, and area-level data at the PCN-level from April 2020 to October 2023 were combined. Population need before the rollout of link workers was measured using reported lack of support from local services in the 2019 General Practice Patient Survey. To assess if rollout reflected need, linear regression was used to relate provision of link workers (measured by full-time equivalent [FTE] per 10 000 patients) in each quarter to population need for support. RESULTS: Populations in urban, more deprived areas and with higher proportions of people from minority ethnic groups had the highest reported lack of support. Geographically these were in the North West and London. Initially, there was no association between need and provision; then from July 2022, this became negative and significant. By October 2023, a 10-percentage point higher need for support was associated with a 0.035 (95% confidence interval = -0.634 to -0.066) lower FTE per 10 000 patients. CONCLUSION: Rollout of link workers has not been sufficiently targeted at areas with the highest need. Future deployments should be targeted at those areas.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".