Ethics framework and recommendations to support capabilities of people with intellectual and developmental disabilities during pandemics
Bibliographic record
Abstract
A growing body of knowledge highlights the negative impact of the COVID-19 pandemic on the health and well-being of many people with intellectual and developmental disabilities (IDDs) and their caregivers. The underlying reasons are not only due to biomedical factors but also ethical issues. They stem from longstanding and pervasive structural injustices and negative social attitudes that continue to devalue people with IDD and that underlie certain clinical decisions and frameworks for public-health policies during this pandemic. Unless these fundamental ethical shortcomings are addressed, pandemic responses will continue to undermine the human rights and well-being of people with IDD. This paper proposes an ethics framing for policy and practices regarding clinical care and public health based on Martha Nussbaum's approach to Capability Theory. Such a framework can reorient healthcare professionals and healthcare systems to support the capabilities of people with IDD to protect, recover, and promote health and well-being. It could be applied during this pandemic and in planning for future pandemics. The paper presents some practical recommendations that follow from applying this framework.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".