Building the capacity of older adults and community: findings from a developmental evaluation of United Way British Columbia’s social prescribing programs for older adults
Bibliographic record
Abstract
INTRODUCTION: Older adults with higher needs are ideal candidates for social prescribing interventions, given the complex and intersectoral nature of their needs. This article describes findings from a developmental evaluation of 19 social prescribing programs for older adults at risk of frailty. METHODS: An evaluation of the programs was conducted from 2020 to 2023. We used data from three components of the evaluation: (1) initial evaluation data collected in 2020 and 2021; (2) program profiles developed in 2022; and (3) co-creation sessions conducted in 2023. RESULTS: From startup until March 2023, the programs served a total of 2544 older adults. The community connectors identified factors at the individual, interpersonal, institutional, community and policy levels that contributed to the successful implementation and delivery of their programs (e.g. physician champions, communities of practice, strong pre-existing relationships with the health care system), as well as challenges (e.g. limited capacity of family physicians, lack of community resources). There was strong agreement among community connectors that successful social prescribing programs should include the following core elements: (1) making connections to needed community resources; (2) co-creation of a wellness plan with long-term clients or clients who require intensive supports; (3) ongoing follow-up and check-ins for clients with wellness plans; and (4) an assessment and triaging process for the prioritization of clients. CONCLUSION: To leverage the full potential of social prescribing interventions, it is essential that programs engage with a range of health and social care providers, that community connectors are skilled and well supported, and that adequate investments are made in the nonprofit and voluntary sector.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".