Une approche éthique de la question du confinement des personnes âgées en contexte de pandémie COVID-19 : la prévention des fragilités face au risque de vulnérabilité
Bibliographic record
Abstract
COVID-19 pandemic particularly affects older people and exposes them to a higher risk of mortality. Containment, social distancing and isolation measures have been implemented to limit viral transmission. While there is a clear rationale for reducing the contagiousness of the infection through this means, the adverse consequences of this social isolation, especially for this heterogeneous, aged and frail people, are difficult to apprehend. In particular, the disruption of the usual support and care ecosystems at home or in institutions may paradoxically increase the frailty of these people and lead to adverse events we wanted to avoid. On the other hand, the risk of a decrease in the older person's empowerment regarding his or her own health and social life decisions requires particular vigilance to prevent the risk of societal ageism. Regarding this population in particular, a possible conflict of values between individual and collective protection on one hand and respect for autonomy and independence on the other hand could exist. This article proposes an ethical reflection on the issue of containment of frail ageing people, based on medical ethics principles, in order to open up positive approaches of vulnerability that guarantee respect for the dignity of the person and equity in care access.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".