Culture Change in Long-Term Care-Post COVID-19: Adapting to a New Reality Using Established Ideas and Systems
Bibliographic record
Abstract
The response to the COVID-19 pandemic in long-term care (LTC) has threatened to undo efforts to transform the culture of care from institutionalized to de-institutionalized models characterized by an orientation towards person- and relationship-centred care. Given the pandemic's persistence, the sustainability of culture-change efforts has come under scrutiny. Drawing on seven culture-change models implemented in Canada, we identify organizational prerequisites, facilitatory mechanisms, and frontline changes relevant to culture change that can strengthen the COVID-19 pandemic response in LTC homes. We contend that a reversal to institutionalized care models to achieve public health goals of limiting COVID-19 and other infectious disease outbreaks is detrimental to LTC residents, their families, and staff. Culture change and infection control need not be antithetical. Both strategies share common goals and approaches that can be integrated as LTC practitioners consider ongoing interventions to improve residents' quality of life, while ensuring the well-being of staff and residents' families.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| 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".