The process of implementing culture change across a city-operated long-term care home and the importance of stakeholder engagement
Bibliographic record
Abstract
This article describes the Quality Improvement (QI) initiative of a culture change model, CareTO. CareTO is a made-in-Toronto, resident-driven, person-centred approach to care that was implemented across all units of a City of Toronto-operated Long-Term Care (LTC) home during the COVID-19 pandemic. The City of Toronto's Seniors Services and Long-Term Care (SSLTC) Division partnered with an external QI team to support the implementation of CareTO at the pilot site. This team employed a multi-method approach (fact-gathering conversations, stakeholder survey, and meeting) to understand how residents, families, and professionals defined CareTO, and identified implementation facilitators, barriers, and priorities. Emerging findings were shared with SSLTC to inform the delivery of CareTO in real time. Results suggested that stakeholder engagement, and collaborations between external partners and municipal governments are an effective means of mobilizing implementation initiatives by encouraging reflection, developing a shared understanding, and refining objectives.
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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 | 0.094 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| 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".