Bibliographic record
Abstract
The long-term care (LTC) sector in Canada has experienced high numbers of COVID-19 deaths. However, there is a paucity of data on the impact of COVID-19 in LTC by different socio- demographic variables and in LTC homes within different regions. Additionally, the question remains as to how exactly and by how much the pandemic has impacted mortality in LTC in comparison to previous years’ mortality. Ranges for expected mortality by sex, province, and age, for the 2020-21 fiscal year were determined by creating forecasts and confidence intervals based on mortality trends in the preceding four fiscal years. These ranges were then compared to the actual mortality data in 2020-21. Comparisons between expected ranges and actual data were also conducted for the number of active residents, admissions, and discharges in LTC by sex, province, and age. Further, mortality ratios were created and studied by sex, province, age, and health region/authority/local health integration network. Overall, the number of deaths in LTC in Canada increased beyond the expected ranges in quarter one and three of 2020-21, and the patterns in death ratios were similar. Increases were exceptional in comparison to the peaks in deaths in previous years for specific variables, but not all variables. Most commonly, the number of active residents and admissions decreased in 2020-21 and the number of discharges from LTC did not change in quarter one and three and decreased in quarter two and four. However, importantly, these trends also varied across variables. This was the first study to comprehensively examine mortality due to COVID-19 in LTC overall, and by multiple socio- demographic variables while elucidating the complexity in the study of mortality in LTC. Further research is required to concretely understand mortality in LTC by different variables and regions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".