ASSESSING THE SCALABILITY OF A COMMUNITY PROGRAM FOR OLDER ADULTS WITH DIABETES: BRIDGING THE RESEARCH–PRACTICE GAP
Bibliographic record
Abstract
Abstract Implementing person-centred, cost-effective, and comprehensive self-management programs for older adults with multimorbidity and their caregivers in the health and social care system is challenging. Innovations that are effective under controlled research conditions often fail to produce similar results when implemented in real world settings to reach larger populations of older adults. Scalability assessment is a promising methodology to reduce this research-practice gap. This study aims to examine the scalability of a self-management intervention for older adults with diabetes and multimorbidity and their caregivers in two Canadian provinces. Provincial working groups, including patient partners, participated in the scalability assessment. The Intervention Scalability Assessment Tool (ISAT) guided data collection and analysis. Multiple methods were used to collect data, including an environmental scan, document review, and interviews with key informants. Provincial workshops were held to review scalability results, determine the program’s scale-up readiness, and identify strategies to enhance scalability. Patient partners, health and social care providers and leaders, provincial decision-makers, and researchers gave high ratings to the readiness of the intervention and its alignment with practice and strategic policy initiatives. The evidence of effectiveness, delivery setting and workforce, and sustainability domains were rated lower. Participants recommended: 1) focusing on high-risk patients, which would be cost effective and likely to demonstrate impact and 2) piloting targeted sites to embed the program within existing health care settings and infrastructure to gather more evidence on program effectiveness and implementation. Collaborative and structured scalability assessments are critical to mobilize innovative programs into health and social care practice.
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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 | 0.142 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 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".