Restoring trust: COVID-19 and the future of long-term care in Canada
Bibliographic record
Abstract
The Royal Society of Canada Task Force on COVID-19 was formed in April 2020 to provide evidence-informed perspectives on major societal challenges in response to and recovery from COVID-19. The Task Force established a series of working groups to rapidly develop policy briefings, with the objective of supporting policy makers with evidence to inform their decisions. This paper reports the findings of the COVID-19 Long-Term Care (LTC) working group addressing a preferred future for LTC in Canada, with a specific focus on COVID-19 and the LTC workforce. First, the report addresses the research context and policy environment in Canada’s LTC sector before COVID-19 and then summarizes the existing knowledge base for integrated solutions to challenges that exist in the LTC sector. Second, the report outlines vulnerabilities exposed because of COVID-19, including deficiencies in the LTC sector that contributed to the magnitude of the COVID-19 crisis. This section focuses especially on the characteristics of older adults living in nursing homes, their caregivers, and the physical environment of nursing homes as important contributors to the COVID-19 crisis. Finally, the report articulates principles for action and nine recommendations for action to help solve the workforce crisis in nursing homes.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".