Looking Back and Moving Forward: Exploring Community Connectors’ Experience With Implementing Social Prescribing
Bibliographic record
Abstract
Social prescribing is a health and social model of care which is emerging globally. It is a multifaceted intervention shaped by various contextual factors that can affect its implementation. Our aim was to describe community connectors’ (link workers or navigators) perceptions and experiences delivering social prescribing programs, with a particular interest in identifying implementation factors or themes. We conducted 11 online semi‐structured interviews with community connectors who delivered social prescribing in British Columbia (BC), Canada. We used directed content analysis, and two authors explored interviews using an implementation perspective. We sorted findings using a deductive approach based on previously published guidance to consider program acceptability, adoption, reach, dose, fidelity, feasibility, and sustainability, and community connectors’ self‐efficacy in delivering the program. We identified factors or themes which could impact on social prescribing implementation, specifically: variability in people’s unmet social needs, identification of community resources, team relationships, and communication. Participants also shared their experiences and perspectives on community connectors’ training, support, and their roles and scope within the continuum of care. At the client level, participants noted some challenges for people to access services because of low income and/or digital literacy. They further provided suggestions for shaping the future of social prescribing. Overall, participants provided valuable insights into social prescribing implementation opportunities and challenges which contribute to understanding community connectors’ role within the wider scope of this quickly emerging health and social model of care.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".