Staff perceptions of the consequences of COVID‐19 on quality of dementia care for residents in Ontario long‐term care homes
Bibliographic record
Abstract
OBJECTIVES: The first wave of the COVID-19 pandemic necessitated extensive infection control measures in long-term care (LTC) and had a significant impact on staffing and services. Anecdotal reports indicate that this negatively affected LTC residents' quality of care and wellbeing, but there is scarce evidence on the effects of COVID-19 on quality of dementia care in LTC. METHODS: From December 2020 to March 2021, we conducted a cross-sectional online survey among staff who worked in LTC homes in Ontario, Canada. Survey questions examined staffs' perceptions of the impact of COVID-19 on dementia quality of care during the initial wave of the COVID-19 pandemic (beginning 1 March 2020). RESULTS: There were a total of 227 survey respondents; more than half reported both worsened overall quality of care (51.3%) and worsening of a majority of specific quality of care measures (55.5%). Measures of cognitive functioning, mobility and behavioural symptoms were most frequently described as worsened. Medical and allied/support staff had the highest odds of reporting overall worsened quality of care, while specialized behavioural care staff and those with more experience in LTC were less likely to. LTC home factors including rural location and smaller size, staffing challenges, higher number of outbreaks and less COVID-19 preparedness were associated with increased odds of perceived worsening of quality of dementia care outcomes. CONCLUSIONS: These findings suggest that COVID-19 pandemic restrictions and related effects such as inadequate staffing may have contributed to poor quality of care and outcomes for those with dementia in LTC.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".