Bibliographic record
Abstract
Social prescribing involves identifying a person’s non-medical, health-related social needs, where they are connected to non-medical supports and services. Since the social prescribing movement is rapidly expanding to over 20 countries globally, students play a role in meaningfully contributing, taking on the connector role, or benefitting. Yet, no studies describe the extent and type of evidence on social prescribing and students. We aim to contribute to the evidence base on social prescribing and investigate the numerous intersections and reciprocal influence between social prescribing and students. We sought to understand (1) the extent and types of evidence on social prescribing and students and (2) the knowledge gaps in the evidence base around social prescribing and students. We included evidence sources where the participants are labelled as students, with no age limits and considered references with interventions that aligns with the conditions set that reflects the definition of social prescribing. Both published and unpublished literature will be included, following our protocol registered on Open Science Framework and the JBI methodology for scoping review. Two independent reviewers completed the data extraction, with a discussion or third reviewer used to address conflicts. Grey literature searches did not yield any applicable evidence sources. We seek to provide information about the participants, concepts, programs, study methods, and outcomes from the 18 evidence sources that met the criteria. Social prescribing is a solution to address the limitations of conventional medical practices where student involvement is crucial as they are the future leaders in research, policy shaping, and implementation of social prescriptions. The authors are involved in the Canadian Social Prescribing Student Collective. This novel study, co-created by students in undergraduate and graduate programs, will encourage the participation of students in social prescriptions, identify specific knowledge gaps, and include student perspectives.
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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 | 0.046 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.035 | 0.037 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".