What healthcare professionals owe us: why their duty to treat during a pandemic is contingent on personal protective equipment (PPE)
Bibliographic record
Abstract
Healthcare professionals' capacity to protect themselves, while caring for infected patients during an infectious disease pandemic, depends on their ability to practise universal precautions. In turn, universal precautions rely on the availability of personal protective equipment (PPE). During the SARS-CoV2 outbreak many healthcare workers across the globe have been reluctant to provide patient care because crucial PPE components are in short supply. The lack of such equipment during the pandemic was not a result of careful resource allocation decisions in the global north, where the short supply could be explained through their high cost. Instead, they were the result of democratically elected governments prioritising low tax regimes over an adequate resourcing of their healthcare delivery systems. Such decisions were made despite global health experts warning about the high probability of pandemics like SARS-CoV2 occurring during our lifetimes. Avoidable allocation decisions by democratically elected political leaders resulted in a lack of sufficient PPE for healthcare professionals. After discussing and discounting various ethical arguments in support of a professional obligation to treat, even without or with suboptimal PPE, I conclude that these policy decisions were sufficiently grave that they provide a sound ethical rationale to justify healthcare workers' refusal to provide care to infected patients.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".