Introduction – COVID-19 and Long-Term Care: What Have We Learned?
Bibliographic record
Abstract
The COVID-19 pandemic has led to thousands of deaths; of these, a disproportionate number has occurred in long-term care settings.The papers presented here deal with a number of issues highlighted by this crisis in several jurisdictions, including Ontario, Quebec and the Netherlands.Analyzing these may give us some insight into what is necessary to prevent this disaster from happening again. RésuméLa pandémie de COVID-19 a fait des milliers de morts.Un nombre disproportionné de ceux-ci a eu lieu dans des établissements de soins de longue durée.Les articles présentés ici traitent d' un certain nombre d' enjeux mis en évidence par cette crise dans plusieurs endroits, dont l'Ontario, le Québec et les Pays-Bas.Leur analyse peut nous donner une idée de ce qui est nécessaire pour empêcher que ce type de catastrophe ne se reproduise.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".