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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".