An Ethical Justification for Expanding the Notion of Effectiveness in Vaccine Post-Market Monitoring: Insights from the HPV Vaccine in Canada
Bibliographic record
Abstract
Health regulators must carefully monitor the real-world safety and effectiveness of marketed vaccines through post-market monitoring in order to protect the public's health and promote those vaccines that best achieve public health goals. Yet, despite the fact that vaccines used in collective immunization programmes should be assessed in the context of a public health response, post-market effectiveness monitoring is often limited to assessing immunogenicity or limited programmatic features, rather than assessing effectiveness across populations. We argue that post-market monitoring ought to be expanded in two ways to reflect a 'public health notion of post-market effectiveness', which incorporates normative public health considerations: (i) effectiveness monitoring should yield higher quality data and grant special attention to underrepresented and vulnerable populations; and (ii) the scope of effectiveness should be expanded to include a consideration of the various social factors that maximize (and minimize) a vaccine's effectiveness at the population level, paying particular attention to how immunization programmes impact related health gradients. We use the case of the human papillomavirus vaccine in Canada to elucidate how expanding post-market effectiveness monitoring is necessary to close the gap between clinical practice and public health, and to ensure that vaccines are effective in a morally relevant sense.
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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 | 0.042 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.063 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.011 |
| 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".