Determining the Intersection of Social Prescribing in Social Work Practice: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Social prescribing is a nonclinical intervention used in various health care settings to improve health outcomes of individuals by attending to the social determinants of health and broader social factors. Prevalence of social prescribing has increased globally over the past decade, leading to the creation of new organizations and networks dedicated to social prescription. Although social workers comprise one of the largest providers of social and mental health services sectors, there remains little guidance how social workers can integrate social prescribing in practice. OBJECTIVE: The objectives of this scoping review are 3-fold. The objectives of this scoping review are to (1) systematically scope the literature on social prescribing and social work and identify scholarly gaps in the literature, (2) identify the role of social work in social prescribing, and (3) describe how social workers are integrating and engaging in social prescribing in clinical practice. METHODS: The review follows the 5-stage scoping review framework from Arksey and O'Malley (2005), which was later enhanced by Levac et al in 2010. The review will examine both academic and grey literature. We will search for studies in the following databases: MEDLINE, Embase, PsycINFO, CINAHL, Social Services Abstracts, and Social Work Abstracts. Grey literature will be searched using Google with a focus on social prescription organizations, social prescription conferences, and Canadian social prescription reports. All studies must be in English and there are no date restrictions. Title and abstract screening, assessment of full-text review, and data extraction will be conducted by 2 independent reviewers. Data will be extracted into a chart format, which will be analyzed for data summarization and synthesis. RESULTS: The results of the study and submission of a manuscript for peer review are expected in October 2025. The results of the scoping review are expected to contribute to an understanding of how social workers employed in health care can integrate social prescribing in their practice. CONCLUSIONS: To the authors' knowledge, this is the first scoping review undertaken on the topic of social prescribing and social work. Findings from the scoping review will inform the future development of guidelines to support the integration of social prescription in social work practice. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/75235.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.099 | 0.097 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.019 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.094 | 0.016 |
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".