Measuring Successes of Social Prescribing
Bibliographic record
Abstract
Social prescribing is a referral mechanism which connects people to non-medical, community and social based activities, which aim to empower an individual to take control of and manage their health and wellbeing (Husk et al., 2020). It has experienced rapid global growth in recent years (Morse et al., 2022) and has been recognised to have the potential to address individual, social and societal determinants of health, by improving access to adequate social support, adequate housing, and financial support, in order to avoid social isolation and loneliness for people (NHS England, 2020). While there is obvious growth in social prescribing services, and clear targets for the implementation of social prescribing, there is a reported lack of evaluation of the services, with recent research concluding that economic evaluation of social prescribing is weak, with limited research and evidence in evaluating the impact of social prescribing (Kiely et al., 2022). This is echoed in communities delivering social prescribing, where difficultly in evaluating social prescribing, along with inadequate evaluation processes have been reported (Mulholland, Galway and Lindsay, 2022). The rapid growth of Social Prescribing has resulted in a need for effective, robust evaluation processes, to determine the impact of social prescribing on the health of individuals, as well as its impact on local communities and associated financial costs. \n \nThis report shares details of a one-day workshop on ?Measuring Successes of Social Prescribing? held in Trinity College Dublin in June 2023. The workshop was hosted by the Research and Evaluation Committee of the All-Ireland Social Prescribing Network (AISPN) and was facilitated by Ms. Pat Tobin from Community Action Network (CAN). It was organised as a follow up to a short break out session held the previous year at the All-Ireland Social Prescribing Network (AISPN) conference, held in 2022. During the conference break out session, delegates were invited to share their experience of outcome measurement in social prescribing, incorporating views from link workers, service managers, funders and academic researchers. This immensely informative event provided insights on the challenges associated with measuring and evaluating social prescribing, and it was clear that there was demand for more debate in this area. A conference report is available here. As a result of the interest in further developing measurement strategies, funding was sought to host a one-day workshop. \n \nThe purpose of the workshop was to bring together those involved in social prescribing across the island of Ireland, to discuss the evaluation of social prescribing, and to make recommendations that would result in improving evaluation processes. With funding support acquired from the Health Research Board in Ireland and the Public Health Agency in Northern Ireland, over 60 attendees were able to take part in the workshop, including social prescribing service users, link workers, social prescribing co-ordinators and funders.
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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 | 0.025 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".