Identifying social prescribing core outcomes using a Delphi approach: findings and future directions
Bibliographic record
Abstract
Although social prescribing is a growing global health and social movement, no Delphi studies have determined which outcomes are critical to assess. Our aim was to identify a core outcome set based on feedback from diverse user groups of people who could be affected by (e.g. adults ≥ 60 years) or who can affect (e.g. providers, researchers) social prescribing. METHODS: Following standard guidelines for Delphi studies, we developed a two-round online survey with a focus on Canadian perspectives. We asked participants to rate 21 outcomes as "critical" (7-9 on a 9-point scale), "important but not critical" (4-6 points) or "not important" (1-3 points). We provide a subgroup description of findings from older adult/family and friend perspectives. RESULTS: Round 1 was completed by 74 people from 10 user groups and Round 2 by 52 people from eight user groups (70% retention). Ratings between rounds were generally consistent. Seven outcomes met the "critical" threshold. No outcomes were excluded. Critical outcomes focused on mental health, physical and social functioning, and wellbeing. Participants commented on environmental (e.g. resources, care delivery) and equity factors. CONCLUSION: This study identified seven critical outcomes to consider in evaluations of social prescribing research and interventions. Future investigations should investigate how contextual and personal factors might influence outcomes and identify specific instruments (e.g. questionnaires, performance-based tests) to assess each outcome. Identification of outcomes is a continuous process, requiring regular updates as results may change due the ongoing evolution of social prescribing and other factors.
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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 | 0.265 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".