Using Implementation Science to Support a Research and Public Policy Sector Older Adult Social Housing Partnership
Bibliographic record
Abstract
Abstract One quarter of the residents in the City of Toronto is comprised of older adults, and this number is expected to continue to grow dramatically over the next few decades. The development of evidence-based interventions to meet the health and social care needs of Toronto’s aging population can be hampered by failing to account for broader implementation considerations that can adversely affect successful uptake. The present initiative provides a case-example of a research and public policy sector partnership that used an implementation approach to co-design an older adult social housing model for low-income older adult groups. Implementation science is the study of the uptake of research evidence into practice. Our team used the Consolidated Framework for Implementation Research (CFIR) to support the planning, implementation and evaluation process of a new social housing model for older adults by: 1) identifying all relevant stakeholders; 2) generating evidence via qualitative interviews/focus groups, a scoping review, secondary data analysis, and an environmental scan; 3) facilitating large scale stakeholder consultation events with older adults, front-line practitioners and other community agencies; 4) supporting the development of an evaluation framework; and 5) providing opportunities for knowledge exchange and transfer across each phase of the initiative. An implementation science approach has augmented the ability of the City of Toronto to optimize the co-creation of housing strategies aimed at improving the overall wellness of vulnerable older adults living in social housing. Further, a number of valuable lessons were learned on how to foster successful research and public policy relationships.
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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 | 0.466 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".