International Evidence on the COVID-19 Deaths of People Who Live in Long-Term Care Facilities
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has had a disproportionate impact, in terms of mortality, on people who live in Long-Term Care Facilities (LTCFs). This study involved compiling data on number of deaths of people who live in LTCFs and analyzing the extent to which differences between countries could be attributed to measures taken to control the spread of COVID-19 to LTCFs or to other factors. The study found that differences in how the data is collected make international comparisons difficult but that there is a clear correlation between number of COVID-19 deaths of residents in LTCFs and number of COVID-19 deaths of people living in the community. The study also found that countries that experienced a particularly high number of deaths in LTCFs during the first COVID-19 wave tended to have lower relative mortality in LTCFs in the subsequent waves, which potentially could be attributed to learning from the initial shock.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".