Initiatives to increase childhood vaccination coverage: an international comparison
Bibliographic record
Abstract
Background Childhood vaccination rates fluctuate over time and do not always meet the levels recommended by the WHO. Objective This study aims to provide an overview of measures countries have introduced to increase vaccination rates. Methods We developed a structured data collection template that was completed by country experts from Europe, Israel, the USA, and Canada. Experts were identified using the European Observatory on Health Systems and Policies' HSPM (Health Systems and Policy Monitor) network. We approached experts from 32 countries and received responses from 22 countries. In the template we asked for measures introduced between 2014 and 2019. The experts were asked to indicate the type of intervention, the target population, possible positive and negative effects, and evidence on effectiveness. The information was collected between September 2019 and January 2020. Results We identified four main types of interventions: restrictive measures for the unvaccinated, financial incentives, measures supporting the logistics of vaccination, and vaccination promotion campaigns. Restrictive measures often involved expanding existing mandatory vaccination policies or limiting access to pre-school activities for unvaccinated children. Financial incentives for healthcare providers showed some positive effects. Regarding logistical support, several countries used schools as alternative vaccination sites, though this presented organisational challenges. Many countries invested in improving knowledge among both healthcare professionals and parents to encourage vaccine uptake. Conclusions Most initiatives implemented in the countries covered focussed on communication and knowledge enhancement. However, there is limited evidence on the impact of these measures on vaccination coverage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".