Using self-determination theory to understand the social prescribing process: a qualitative study
Bibliographic record
Abstract
BACKGROUND: was a SP project, which was implemented within 11 community health centres (CHCs) situated across Ontario, Canada. AIM: To explore how SP as a process facilitates positive outcomes for patients. DESIGN & SETTING: Qualitative methods were used. Eighteen focus groups were conducted at CHCs or by video-conferencing, and involved 88 patients. In addition, eight in-depth telephone interviews were undertaken. METHOD: Interviews and focus groups were transcribed verbatim, and analysed thematically using a theoretical framework based on self-determination theory (SDT). RESULTS: Participants who had received social prescriptions described SP as an empathetic process that respects their needs and interests. SP facilitated the patient's voice in their care, helped patients to develop skills in addressing needs important to them, and fostered trusting relationships with staff and other participants. Patients reported their social support networks were expanded, and they had improved mental health and ability in self-management of chronic conditions. Patients who became involved in SP as voluntary 'health champions' reported this was a positive experience and they gained a sense of purpose by giving back to their communities in ways that felt meaningful for them. CONCLUSION: SP produced positive outcomes for patients, and it fits well within the community health centre model of primary care. Future research should examine the impact on health outcomes and examine the return on investment of developing and implementing SP programmes.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".