MétaCan
Menu
Back to cohort
Record W7052483411

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

2021· article· en· W7052483411 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPersonal careHealth careVisitor patternAdvance care planningPersonal protective equipmentPandemicPersonal lifePersonal hygiene
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0030.006
Scholarly communication0.0050.005
Open science0.0010.002
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.035
GPT teacher head0.327
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicParticle Detector Development and PerformanceFrench-language works237,207