Long-Term Care Staffing Policies Pre-COVID-19 and Pandemic Responses: A Case Comparison of Ontario and British Columbia
Bibliographic record
Abstract
À la fin de mai 2020, les cas de COVID-19 chez les résident·es des établissements de soins de longue durée (SLD) en Ontario représentaient 5 157 des 28 499 cas de la province. En Colombie-Britannique (C.-B.), il y avait 339 cas chez les résident·es de ces établissements, comparativement à un total provincial de 2 562 cas. Bien que le secteur des SLD de ces deux provinces présente certaines différences, cet article passe en revue les politiques de dotation en personnel des SLD dans chacune des deux provinces avant la pandémie et compare leurs mesures de prévention de la COVID-19 ayant trait à la dotation pour 2020. Aux politiques de l’Ontario avant 2020 correspondent des ratios personnel-patients inférieurs à ceux de la Colombie-Britannique, ce qui peut avoir eu un effet limitant sur les réactions de l’Ontario à la pandémie. L’établissement de normes ou de lignes directrices ainsi qu’une modification du financement pourraient améliorer la résilience du secteur des SLD en matière de dotation en personnel.
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 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 | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".