Bibliographic record
Abstract
Across Canada, the long-term care sector has received increased attention since the devastating impact of the COVID-19 pandemic. The now often-cited statistic - 80% of deaths in the first wave occurred among individuals residing in institutional long-term care - is tragic enough and is only compounded by the fact that the number of deaths in long-term care were still higher in the second wave in all but two provinces. Many have argued that the impact of the pandemic was amplified in the institutional long-term care sector because of a number of long-standing shortfalls in funding, space, staffing and infrastructure. For example, Canadian provinces had lower average direct hours of care (three hours per day) provided to residents in long-term care facilities than even the average of four hours per day provided in the United States (Hsu et al. 2016).
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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".