Lessons from the COVID-19 Pandemic for Long-Term Care: Where Do We Go Next?
Bibliographic record
Abstract
Even before the COVID-19 pandemic, I would often hear colleagues who are intimately familiar with our health and social care system remark that they would never allow themselves or those closest to them to end up in long-term care. Sadly, the conversation often progressed to an acknowledgment that more desirable alternatives to long-term care for the most part lie outside our publicly supported care system and are only accessible to those with the means. And then we had the pandemic. For too many it turned what was often dreary and uninspiring care into a modern hell - so awful that two Canadian provinces called in the military to restore care in their worst-hit homes (Howlett 2021). There can be no doubt that the challenges that we face in providing dignified, respectful care to all our seniors have been decades in the making. It would be wrong to simply blame the long-term care homes, and it would be a travesty to lay the blame on individual care providers. On the contrary, those working in long-term care have continued to do their best, against the odds. In the early stages of the pandemic, they were not given the support that they deserved, and many paid a high personal price for their service.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.017 | 0.037 |
| Insufficient payload (model declined to judge) | 0.014 | 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".