A Review of the Medical Ethics Surrounding COVID-19 Lockdowns in Personal Care Homes and the Impacts on Those Living with Dementia: Ethics, COVID and Personal Care Homes
Bibliographic record
Abstract
The impacts of the COVID-19 pandemic on personal care homes has played out daily in the media headlines across the country. Although Manitoba seemed to avoid the worst of this early on in the pandemic, recently the province has seen a disturbing surge in overall case levels (Government of Manitoba, 2020). This rise in COVID-19 cases in Manitoba has forced personal care homes to once again lockdown and restrict not only all visitor access but also limit social interaction among the residents themselves. When looking at this situation broadly, locking down all personal care homes seems to be like the obvious decision to make. Residents in personal care homes represent one of our most vulnerable populations and the virus has been shown to spread quickly with serious medical impacts to this group. Significant virus spread in these personal care homes could easily overwhelm our healthcare system and lead to unnecessary deaths. Clearly the decision to lockdown the personal care homes can be readily justified as a means of protecting not only this vulnerable group but also supporting the larger community who either provide healthcare or are requiring healthcare for other non-COVID-19 related reasons. While the above is true, the decision to do this is far from being straight forward. The impacts of these lockdowns are far reaching and go way beyond just the containment of the virus. There is a fine balance between maintaining the emotional and mental well-being of an individual living with dementia and managing the physical health of the greater population.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".