High death rate of older persons from COVID-19 in Quebec (Canada) long-term care facilities: chronology and analysis
Bibliographic record
Abstract
Purpose Among the ten Canadian provinces, Quebec has experienced the most significant excess mortality of older persons during COVID-19. This practice paper aims to present the chronology of events leading to this excess mortality in long-term care facilities (LTCFs) and a comprehensive analysis of the phenomenon. Design/methodology/approach Documented content from three official sources: daily briefings by the Quebec Premier, a report from the Canadian Armed Forces and a report produced by Royal Society of Canada experts were analysed. Findings Two findings emerge: the lack of preparation in LTCFs and a critical shortage of staff. Indeed, the massive transfer of older persons from hospitals to LTCFs, combined with human resources management and a critical shortage of permanent staff before and during the crisis, generates unhealthy living conditions in LTCFs. Originality/value To our knowledge, this paper is the first to analyse official Quebec and Canadian statements concerning COVID-19 from the angle of quality of life and protection of older adults in LTCFs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".