Non-Professional Healthcare Workers and Ethical Obligations to Work during Pandemic Influenza
Bibliographic record
Abstract
Most academic papers on ethics in pandemics concentrate on the duties of healthcare professionals. This paper will consider non-professional healthcare workers: do they have a moral obligation to work during an influenza pandemic? If so, is this an obligation that outweighs others they might have, e.g., as parents, and should such an obligation be backed up by the coercive power of law? This paper considers whether non-professional healthcare workers—porters, domestic service workers, catering staff, clerks, IT support workers, etc.—have an obligation to work during an influenza pandemic. It uses data collected as part of a study looking at the attitudes of healthcare workers to working during a pandemic to suggest the philosophical arguments explored. These include: being in a position to do good, the ethics of work, competing obligations to family members and in particular to children and the obligations of citizens in a state of national emergency. We also look at whether compulsory measures are justified to support a national health service during a health emergency. We conclude that even if they are, compulsion should not be restricted to non-professionals who happen to be working in the health service at the time. Rather, compulsion involving a larger pool of people with the relevant skills and abilities is more equitable.
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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 | 0.033 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".