Mandatory infant & childhood immunization: Rationales, issues and knowledge gaps
Bibliographic record
Abstract
Globally, infant and childhood vaccine uptake rates are not high enough to control vaccine preventable diseases, with outbreaks occurring even in high-income countries. This has led a number of high-, middle-and low income countries to enact, strengthen or contemplate mandatory infant and/or childhood immunization to try to address the gap. Mandatory immunization that reduces or eliminates individual choice is often controversial. There is no standard approach to mandatory immunization. What vaccines are included, age groups covered, program flexibility and rigidity e.g. opportunities for opting out, penalties or incentives, degree of enforcement, and whether a compensation program for causally associated serious adverse events following immunization exists vary widely. We present an overview of mandatory immunization with examples in two high- and one low-income countries to illustrate variations, summarize limited outcome data related to mandatory immunization, and suggest key elements to consider when contemplating mandatory infant and/or child immunization. Before moving forward with mandatory immunization, governments need to assure financial sustainability, uninterrupted supply and equitable access to all the population. Other interventions may be more effective and less intrusive than mandatory. If mandatory is implemented, this needs to be tailored to fit the context and the country's culture.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".