Experience and perceptions of Social Prescribing interventions; a qualitative study with people with long-term conditions, link workers and health care providers
Bibliographic record
Abstract
<ns5:p>Background Long-term conditions (LTC) are a leading cause of reduced quality of life and early mortality. People with LTC are living longer with increasing economic and social needs. Novel patient centred care pathways are required to support traditional medical management of these patients. Social Prescribing (SP) has gained popularity as a non-medical approach to support patients with LTC and their unmet health needs. The current focus group study aims to explore the experiences and perceptions to SP interventions from the perspective of people with long-term conditions, link workers, healthcare providers and community-based services. Methods Six toeight participants will be recruited into three specific 60 to 90 minute focus groups relative to their role as a patient, link worker and community-based service. 8 to12 participants with a Health care provider and GP background will be interviewed individually online. The participants within these focus groups and semi-structured interviews will be invited to provide opinions on what factors they think are important to the successful implementation of a SP service from their respective stakeholder positions. The data will be recorded and exported to NVivo software for further analysis using Thematic Reflexive analysis methods. Coded categorical data will inform emerging themes from which a narrative summary will be consolidated and presented for dissemination. Conclusion The conclusions made from this study will help inform the next study, which will aim to develop a pilot SP service for patients with long-term musculoskeletal conditions as part of an overall larger project.</ns5:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".