Identifying models of care to support residents in long-term care homes (LTCHs) both during and beyond COVID-19
Bibliographic record
Abstract
Long-term care homes (LTCHs) implemented various models of care during the COVID-19 pandemic. The purpose of this study was to identify these models of care and provide suggestions on best practices that could be integrated into LTCHs in efforts to improve resident care. The project included a quantitative survey and semi-structured key informant interviews with LTCH managers across Canada. Our objectives were to 1) identify models of care that were used to support resident care in Canadian LTCHs during the COVID-19 pandemic and to describe their intervention components, processes of implementation, and perceived impact; 2) determine whether LTCHs planned to sustain models of care implemented during the COVID-19 pandemic. Our results show that the most frequently reported models of care were related to healthy food options, exercise, music and art programs, and planned social activities for residents. Five barriers were identified in relation to implementing these models of care, which included: lack of funding, resources, or staffing; staff not being familiar with/reluctant to use the model; lack of resident buy-in; fear of COVID-19; and pandemic regulations. Common facilitators to implementation were also identified and included: staff support; resident/family buy-in; funding, legislation and/or resources provided; familiarity with model prior to COVID-19; and collaboration with other LTCHs. LTCHs perceived the models to be effective and planned to sustain most implemented models. LTCH managers discussed the need for funding and legislation to improve LTCHs and support the implementation of promising models of care. This study provides insight into the models of care implemented during the pandemic crisis period in Canadian LTCHs, how effective they were perceived to be, and plans for sustainment beyond the pandemic period.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".