DIGITAL SOCIAL PRESCRIBING LITE: ADDRESSING BARRIERS OF PREVENTIVE INTEGRATED CARE THROUGH COMMUNITY ENGAGEMENT
Bibliographic record
Abstract
Abstract Digital social prescribing solutions attempt to integrate health and social care by targeting both preventive and complex health challenges. However, a lack of community engagement impedes referral uptake. This paper uses “customer discovery” to explore and address current barriers of digital social prescribing from the perspectives of service users and staff of health and community sectors. Phase 1 prioritized community perspectives and led to a community-generated place-based wellness model to align interests across sectors. Phase 2 conducted market research with over 70 health and community stakeholders in Metro Vancouver through snowball sampling. Insights were gathered on current referral processes, barriers to social prescribing, and stakeholder perspectives. From the insights shared, it was evident that achieving integrated care faces twin challenges of resource allocation and staff burnout. Funding-related barriers include inadequate partnership, intermittent funds, lack of billable codes, variations in cost, and lack of monitoring and evaluation. Staffing-related barriers include time-intensive processes, outdated information, inappropriate referrals and lack of guidance for quality improvement. Based on insights from stakeholders, initial cross-sectoral implementation may fruitfully focus on preventing the effects of social isolation. This “lite” version of digital social prescribing requires an automated referral and monitoring platform which allows prevention for the masses while flagging complex cases for intervention. This reduces clinician burden and empowers community sector to support wellbeing. The place-based model “Connectedness, feeling At home, and joyful Play” was incorporated into a web app. Digital social prescribing lite may pave the way for data-driven, organic scaling up of preventive integrated 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".