Establishing Internationally Accepted Conceptual and Operational Definitions of Social Prescribing Through Expert Consensus: A Delphi Study Protocol
Bibliographic record
Abstract
Introduction: There is currently no agreed definition of social prescribing. This is problematic for research, policy, and practice, as the use of common language is the crux of establishing a common understanding. Both conceptual and operational definitions of social prescribing are needed to address this gap. Therefore, the aim of the study that is outlined in this protocol is to establish internationally accepted conceptual and operational definitions of social prescribing.Methodology: A Delphi study will be conducted to develop internationally accepted conceptual and operational definitions of social prescribing with an international, multidisciplinary panel of experts. It is anticipated that this study will involve approximately 40 participants (range = 20-60 participants) and consist of 3-5 rounds. Consensus will be defined a priori as ≥80% agreement.Discussion: Not only will these definitions serve to unite the social prescribing community, but they will also inform research, policy, and practice. By laying the groundwork for the formation of a robust evidence base, this foundational work will support the advancement of social prescribing and help to unlock the full potential of the social prescribing movement.Conclusion: This important work will be foundational and timely, given the rapid spread of the social prescribing movement around the world.
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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 | 0.242 | 0.138 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".