Theories used to develop or evaluate social prescribing in studies: a scoping review
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to provide an overview of how theories were used in the development or evaluation of social prescribing (SP) intervention studies. BACKGROUND: SP describes a patient pathway where general practitioners (GPs) connect patients with community activities through referrals to link workers. This review seeks to understand the explanations provided for the outcomes and implementation process of SP. INCLUSION CRITERIA: Studies using a defined theory to develop or evaluate a specific SP intervention in primary care and the community sector. METHODS: of July 2022: PubMed, ASSIA, Cochrane, Cinahl, PsycINFO, Social Care Online, Sociological Abstracts, Scopus, and Web of Science. The search only considered English language texts. Additional literature was identified by searching relevant web pages and by contacting experts. The selection of sources and the data extraction was done by two reviewers independently. RESULTS: The search resulted in 4240 reports, of which 18 were included in the scoping review. Of these, 16 were conducted in the UK, one in Canada and one in Australia. The majority of reports employed a qualitative approach (11/18). Three were study protocols. 11 distinct theories were applied to explain outcomes (4 theories), differences in outcomes (3 theories), and the implementation of the intervention (4 theories). In terms of practical application, the identified theories were predominantly used to explain and understand qualitative findings. Only one theory was used to define variables for hypothesis testing. All theories were used for the evaluation and none for the development of SP. CONCLUSION: The theories influenced which outcomes the evaluation assessed, which causal pathway was expected to generate these outcomes, and which methodological approaches were used. All three groups of theories that were identified focus on relevant aspects of SP: fostering positive patient/community outcomes, addressing inequalities by considering the context of someone's individual circumstances, and successfully implementing SP by collaboratively working across professions and institutional boundaries. Additional insight is required regarding the optimal use of theories in practical applications.
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 | 0.248 | 0.473 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.105 | 0.061 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".