Developing an evidence base to inform retirement home policy development using an equity and diversity lens: a mixed methods study protocol
Bibliographic record
Abstract
BACKGROUND: Retirement homes are an essential support for many older adults as they age. However, retirement homes may not be an option for all, particularly adults with low socioeconomic status or those from linguistic or visible minority groups, in jurisdictions where there are high out-of-pocket costs and limited availability of language- and culturally-concordant home options. Research examining health inequities in facility-based care for older adults has largely focused on the long-term care sector leaving a gap in knowledge and understanding about the care experiences and health outcomes of retirement home residents. The overall aim of this project is to build an evidence base and provide policy recommendations to improve equity in the retirement home sector in Ontario, Canada. METHODS: We will conduct a multi-phase sequential mixed methods study involving both quantitative and qualitative data. Phase 1 will involve consulting on social and systemic barriers to equity in RHs with our Community Advisory Committee members and advocacy group collaborators. In phase 2, we will lead retrospective cohort studies using Ontario health administrative data to describe and evaluate differences in rates of RH vs. LTC use and differences in healthcare outcomes. In Phase 3, we will conduct semi-structured interviews with older adults and their family partners preparing to enter or having already entered, facility-based care within the prior 6 months to investigate how individuals' social identity, including language and income, and pragmatic considerations shape decision-making regarding the transition to facility-based care, including choices between retirement homes and long-term care. Findings from Phases 1,2 and 3 will be contextualized in Phase 4 with decision-maker knowledge users, such as from the Retirement Home Regulatory Authority and Ontario Ministries of Health, Long-term Care, and Seniors and Accessibility. DISCUSSION: This project will leverage the existing momentum for long-term care reform where evidence of health inequities is well documented, to engage in research in a neighbouring sector that serves older adults with increasingly similar health profiles and complexities. We will disseminate contextualized policy options to both provincial-level decision-makers and community knowledge user partners to help inform future work on equity in retirement homes at different levels of governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".