Contextualizing Ethics: Ventilators, H1N1 and Marginalized Populations
Bibliographic record
Abstract
If the H1N1 pandemic worsens, there may not be enough ventilated beds to care for all persons with respiratory failure. To date, researchers who explicitly discuss the ethics of intensive care unit admission and the allocation of ventilators during an influenza pandemic have based criteria predominantly on the principles of utility and efficiency, that is, promoting actions that maximize the greatest good for the greatest number of people. However, haphazardly applying utility and efficiency potentially disadvantages marginalized populations who might be at increased risk of severe reactions to H1N1. In Canada, Aboriginals represent 3% of Canadians, yet 11% of H1N1 cases requiring hospitalization involve Aboriginal persons. Aboriginal persons suffer from high rates of obesity due to socio-economic inequalities. Obesity is also a risk factor for severe H1N1 reactions. Yet, since obesity is found to increase the duration of stay in ventilated beds and a long stay is not considered an optimal use of ventilators, applying the principles of utility and efficiency may magnify existing social inequalities. Although promoting utility and efficiency is important, other ethical principles, such as equity and need, require thoughtful consideration and implementation. Furthermore, since public resources are being used to address a public health hazard, the viewpoints of the public, and specifically stakeholders who will be disproportionately affected, should inform decision-makers. Finally, giving attention to the needs and rights of marginalized populations means that ventilators should not be allocated based on criteria that exacerbate the social injustices faced by these groups of people.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".