Changing Care: Applying the Transtheoretical Model of Change to Embed Equity, Diversity, and Inclusion in Long-Term Care Research in Canada
Bibliographic record
Abstract
Healthcare policy reform is evident when considering the past, present and future of long-term care (LTC) in Canada. Some of the most pressing issues facing the LTC sector include the changing demographic composition in Canadian LTC homes, minimal consideration for the role of intersectionality in LTC data collection and analysis, and the expanding need to engage diverse participants and knowledge users. Using the Transtheoretical Model of Change (TTMC) as a framework, we consider opportunities to address intersectionality in LTC research. Engaging diverse knowledge users in LTC (e.g., unpaid caregivers, paid care staff), community (e.g., advocacy groups, service providers) and policy decision-makers (e.g., provincial government) is crucial. Empowering individuals to participate, modifying environments to support engagement, and facilitating ongoing partnerships with knowledge users are critical aspects of change efforts. Addressing structural barriers (e.g., accessibility, capacity, jurisdictional policies, and mandates) to research in LTC is also essential. The TTMC offers a framework for planning and enacting individual, organizational, and system-level changes for the future of LTC.
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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 | 0.059 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.023 | 0.026 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| 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".