Mandatory immunization: Empirical examination of governance instruments in 28 Global NITAG Network (GNN) countries
Bibliographic record
Abstract
Abstract Background In the Global Vaccine Action Plan 2017 Assessment Report, WHO's SAGE noted need to understand ways in which legislation and regulation are used to advance or undermine immunization. The NITAG Environmental Scan Project sought to address this in a pilot study. Methods Data was collected via a secure online survey of GNN members (40 countries Sept 2018). Respondents reporting a mandatory element were asked: (1) what vaccinations were required by law; and (2) what population groups were subject to mandates; (3) what grounds, if any, were available for requesting exemptions. Results 28 (70%) countries responded, representing every WHO region and World Bank income level. While mandatory immunization programs / elements within broader NIPs were relatively common, jurisdictions varied with respect to immunizations required, population groups affected, grounds for exemptions, and penalties for non-compliance. We observed some loose associations with geography and income level. Children were the most common population group subject to mandates at some stage of childhood development (28/28); healthcare workers were second (8/15 (53%)). Sanctions for failure to immunize varied broadly, ranging from no penalty, to loss of access to social services e.g. admission to school, monetary fines, and incarceration. A variance between countries as to how strictly immunization mandates are enforced was noted. Conclusions A variety of approaches existed ranging from Narrow/Permissive to Broad/ Inclusive in scope with enforcement mapping loosely to this continuum from Loose/Permissive to Tight/Coercive. Jurisdictions with few/no vaccines mandated, and few/no target groups identified, Loose approach is expected; for those closer to Broad approach, Tighter controls expected. Coercive measures may be 'positive' (vaccination as a gateway to public services, with possible work-arounds), or 'negative' (failure to vaccinate = penalties).
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".