Ethical Vaccine Distribution Planning for Pandemic Influenza: Prioritizing Homeless and Hard-to-Reach Populations
Bibliographic record
Abstract
The manner in which limited vaccines are distributed during a pandemic is an ethical issue. The utility principle has been used to argue priority be given to certain individuals based on factors such as the epidemiology of the spread of disease and maintaining the functioning of society. The equity principle has been used to encourage fair practices that account for the economic and social costs of all decisions made. We argue that both principles are met through priority vaccination of homeless individuals, as this strategy protects a medically vulnerable population while reducing the chances of transmission to others as they move through populated urban spaces. We begin by reviewing debates around ethical vaccine distribution. We then argue the homeless are a medically high-risk population who may contribute to the spread of disease through their mobility. As immunization rates are generally lower among the homeless and many do not access mainstream health care, we argue that community vaccine clinics must be used to reach these individuals. We provide support by analyzing Toronto Public Health’s operation of vaccine clinics in shelters and drop-in centres during pH1N1 and conclude that this strategy is effective for immunizing homeless individuals, bringing together the equity and utility principles.
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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 | 0.032 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| 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".