Have social prescribing referrals reduced appointment pressures on primary care? Evidence from Electronic Healthcare Records
Bibliographic record
Abstract
BACKGROUND: One in five general practice appointments are for social rather than medical reasons. To alleviate these pressures on primary care, NHS England mandated the national roll-out of social prescribing link workers in July 2019. AIM: To access whether the national roll-out of social prescribing link workers reduced GP appointments. METHOD: We use electronic healthcare records (Clinical Practice Research Datalink) of 15.7 million patients across 1461 practices in England from 2016 to 2021, representing around 28% of England's population. We create a patient-level quarterly panel dataset (T = 22, N = approx. 240 million) containing counts of appointments with a GP and interactions with social prescribing link workers identified via specific SNOMED codes. We adopted a staggered difference-in-difference approach, where we have treated, not yet treated, and never treated patients for each time point. Patients can be referred (treated) to the NHS Social Prescribing Scheme from Q3 of 2019 onwards (t = 16). We omit 226 practices that implemented separate social prescribing programmes prior to national rollout. RESULTS: Patients referred to social prescribing subsequently have 1.13 fewer appointments per quarter with a GP (95% CI = -1.21 to -1.03) than those not referred. There was a total of 244 626 freed-up appointments with a GP linked to social prescribing referrals. CONCLUSION: Early analysis of the national roll-out of social prescribing link workers has demonstrated that NHS England has met its intended aim of reducing GP appointments. This has the potential to reduce pressures on primary care by addressing patients' social needs.
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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 | 0.035 | 0.318 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".