Patient experience with Social Prescribing Program in Ontario, Canada
Bibliographic record
Abstract
Context Social Prescribing (SP) is an approach to help individuals address their health and social needs wherein a healthcare practitioner refers patients to non-clinal services in the community. Models of SP vary, and the experience of patients across these models is less known. Objective To describe patients’ experience in a SP program where patients were randomly allocated to a holistic, patient centered, bilingual, longitudinal navigation support (Access to Resources in the Community (ARC) model) or signposting to Ontario-211’s free provincial online and dial-in navigation service that provides information and referral to community resources. Study design, setting, population Qualitative study using semi-structured interviews with 32/326 Ontarians participating in the ARC-211 randomized control trial (2019-2020). Analysis Interviews were thematically analyzed with inductive/deductive hybrid approach, using a coding scheme adapted from Levesque’s Access framework and free coding to identify navigators’ approaches that influenced patient’s barriers to access. Results Participants were females (72%), >65 years (28%), Francophone (40%), < university degree (66%), not working (77%) and reported mental health, healthy lifestyle and social/financial related needs. ARC: All (N=17) participants used navigation. Findings were summarized across 5 themes of access and mapped to navigator’s approaches. Participants reported that ARC navigator provided informational, outreach and long-term emotional support that encouraged, motivated and empowered them in their journey to overcome access barriers. Navigator’s approaches helped participants improve their ability to identify their health/social need/s and set priorities, to seek health care services, to reach the referred community service, to obtain affordable service, and improved their self-confidence, trust, self-efficacy and readiness to engage with community services. 211: Narratives indicated Ontario-211 users (3/15) appreciated information provided and active listening by 211 navigators, and the regular updates of 211 online directory. Most non-users did not recall 211 while others were discouraged due to lack of resources and clear web-information, technology challenges, and high cost of services. Conclusion Findings suggest that SP in any form is beneficial. 211 is helpful but has limitations and ARC model although requiring a culture shift appears promising to improve access and reduce inequities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".