Social prescribing for children and youth: A scoping review protocol
Bibliographic record
Abstract
Social prescribing is suited to all age groups, but it is especially important for children and youth, as it is well understood that this population is particularly vulnerable to the effects of the social determinants of health and health inequities, and that intervening at this stage of life has the greatest impact on health and wellbeing over the life course. While this population has largely been neglected in social prescribing research, policy, and practice, several evaluations of social prescribing for children and youth have emerged in recent years, which calls for a review of the evidence on this topic. Thus, the objective of this scoping review is to map the evidence on the use of social prescribing for children and youth. This review will be conducted in accordance with the JBI methodology for scoping reviews and will be reported in line with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The search strategy will aim to locate both published and unpublished literature. No language or date restrictions will be placed on the search. The databases to be searched include MEDLINE (Ovid), CINAHL (EBSCO), Embase (Ovid), PsycINFO (Ovid), AMED (Ovid), ASSIA (ProQuest), Sociological Abstracts (ProQuest), Global Health (Ovid), Web of Science (Clarivate), Epistemonikos, JBI EBP Database (Ovid), and Cochrane Library. Sources of gray literature to be searched include Google, Google Scholar, Social Care Online (Social Care Institute for Excellence), SIREN Evidence and Resource Library (Social Interventions Research and Evaluation Network), and websites of social prescribing organizations and networks. Additionally, a request for evidence sources will be sent out to members of the Global Social Prescribing Alliance. Two independent reviewers will perform title and abstract screening, retrieval and assessment of full-text evidence sources, and data extraction. Data analysis will consist of basic descriptive analysis. Results will be presented in tabular and/or diagrammatic format alongside a narrative summary.
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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 | 0.103 | 0.078 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.019 | 0.017 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.133 | 0.025 |
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".