Preventable? Long-Term Care Policy Successes and Failures During COVID-19 Pandemic: A Scoping Literature Review
Bibliographic record
Abstract
Abstract The number of older adults who live in long-term care (LTC) is expected to increase worldwide. The COVID-19 pandemic has caused serious consequences in Canadian LTC homes, while homes in China and Japan reported minimal infection and death rates in residents. The differences in LTC policies may be one of the contributors. The purpose of this literature review was to identify elements of the LTC policies that might have impacted COVID-19 outcomes in LTC homes in Canada, China, and Japan. A scoping review was conducted following the framework proposed by Arksey and O’Malley. Scholarly articles and grey literature published between January 2015 and June 2020 were identified in six databases, four in English (CINAHL, Scopus, ProQuest, and PubMed), one in Chinese (CNKI), and one in Japanese (CiNii), using MeSH terms for LTC and health policy. Grey literature was identified using Google. Data were extracted, summarized and common themes identified through content analysis. A total of 52 articles and 26 grey sources were included in the review based on determined inclusion criteria. They were research articles, reviews, government or association reports, policy briefs, policy documents, and guides. Four common themes of challenges emerged: caregiver workforce, service provision, funding, and physical environments. Three sub-themes were identified for caregiver workforce and service provision. Differences in COVID-19 consequences in LTC homes in the three countries seem to be related mainly to the challenges with the caregiver workforce and the lack of funding. The result suggests Improvements of LTC policies are required, especially in Canada.
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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 | 0.020 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".