Embedding Social Prescribing in Primary Care in England and Scotland: A Qualitative Study of Experiences, Roles, Challenges, and Sustainability
Bibliographic record
Abstract
Background: In the last decade, social prescribing through link workers based in general practice has become a major policy in the UK, but little is known about the implementation of this strategy. We aimed to explore the roles, challenges, and effects of social prescribing link workers (SPLWs) across different models of employment, organisation, and management in England and Scotland. Methods: In this qualitative study, we conducted semi-structured interviews with stakeholders in two regions in England (National Institute for Health and care Research [NIHR] Applied Research Collaboration [ARC] West and NIHR ARC North East and North Cumbria) and two in Scotland (National Health Service [NHS] Greater Glasgow & Clyde and NHS Lothian). Stakeholders were participants actively involved in SPLW activities and comprised patients aged 18 years or older receiving SPLW support; individual SPLWs; general practice staff who referred patients to the SPLW; leads of voluntary, community, and social enterprises (VCSEs) employing or hosting SPLWs; VCSE leads not employing or hosting SPLWs but supporting referred patients; and strategic leaders working with SPLW services. Purposive sampling was used to ensure diversity in patients' age, gender, and socioeconomic deprivation (according to the Index of Multiple Deprivation in England or Sottish Index of Multiple Deprivation in Scotland) and variation in stakeholder roles. Interviews were continued until data saturation was reached. Transcribed interviews were analysed reflexively using inductive thematic analysis. Findings: Between Feb 16 and Nov 15, 2024, we conducted interviews with 130 stakeholders: 36 SPLWs, 28 patients, 28 referring professionals, ten VCSE leads hosting SPLWs, 14 VCSE leads not hosting SPLWs, and 14 strategic leads. Patient ages ranged from 18 years to older than 65 years; 16 (57%) patients were female, 12 (43%) were male, 26 (93%) were of White British or Scottish ethnicity, one (4%) was of south Asian ethnicity, and one (4%) was of mixed or multiple ethnicity. Six key themes emerged. The first was varied backgrounds, with SPLWs typically coming from the voluntary sector or NHS, driven by a desire to empower patients. The second was changing role, with SPLWs now seeing many more patients with highly complex needs, including the social determinants of health in deprived areas, than they did previously. The third theme was therapeutic relationship, whereby building empathic relationships was crucial but depended on SPLWs' backgrounds, training, and support. The fourth theme, benefits of the SPLWs, was based on SPLWs helping patients to build confidence and increase their independence and, in general practitioners, reducing moral distress and possibly reducing workload. The fifth theme was employment models, organisation, and management, with integration into general practices facilitated by SPLWs working in one or two practices and with robust organisational support. For the final theme, challenges and sustainability, the challenges of the SPLW role included job retention, burnout, variable pay levels, and few opportunities for career progression; the sustainability of this role was threatened by short-term funding, increased service demand, and financial cuts to essential services. Interpretation: This study highlights the vital role of SPLWs in England and Scotland and the need for stable support and funding amid ongoing financial challenges in the statutory and voluntary sectors. Funding: National Institute for Health and Care Research.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".