Social Prescribing Outcomes for Trials (SPOT): Protocol for a modified Delphi study on core outcomes
Bibliographic record
Abstract
PURPOSE: This is a study protocol to co-create with knowledge users a core outcome set focused on middle-aged and older adults (40 years+) for use in social prescribing research. METHODS: We will follow the Core Outcome Measures in Effectiveness Trials (COMET) guide and use modified Delphi methods, including collating outcomes reported in social prescribing publications, online surveys, and discussion with our team to finalize the core outcome set. We intentionally center this work on people who deliver and receive social prescribing and include methods to evaluate collaboration. Our three-part process includes: (1) identifying published systematic reviews on social prescribing for adults to extract reported outcomes; and (2) up to three rounds of online surveys to rate the importance of outcomes for social prescribing. For this part, we will invite people (n = 240) who represent the population experienced in social prescribing, including researchers, members of social prescribing organizations, and people who receive social prescribing and their caregivers. Finally, we will (3) convene a virtual team meeting to discuss and rank the findings and finalize the core outcome set and our knowledge mobilization plan. CONCLUSION: To our knowledge, this is the first study designed to use a modified Delphi method to co-create core outcomes for social prescribing. Development of a core outcome set contributes to improved knowledge synthesis via consistency in measures and terminology. We aim to develop guidance for future research, and specifically on the use of core outcomes for social prescribing at the person/patient, provider, program, and societal-level.
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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 | 0.254 | 0.247 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.111 | 0.030 |
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".