Diagnosing the determinants of vaccine hesitancy in specific subgroups: The Guide to Tailoring Immunization Programmes (TIP)
Bibliographic record
Abstract
Despite relatively high vaccination coverage rates in the European Region, vaccine hesitancy is undermining individual and community protection from vaccine preventable diseases. At the request of its European Technical Advisory Group of Experts on Immunization (ETAGE), the Vaccine-preventable Diseases and Immunization Programme of the WHO Regional Office for Europe (WHO/EURO) developed tools to help countries address hesitancy more effectively. The Guide to Tailoring Immunization Programmes (TIP), an evidence and theory based behavioral insight framework, issued in 2013, provides tools to (1) identify vaccine hesitant population subgroups, (2) diagnose their demand- and supply-side immunization barriers and enablers and (3) design evidence-informed responses to hesitancy appropriate to the subgroup setting, context and vaccine. The Strategic Advisory Group of Experts on Immunization (SAGE) through its Working Group on Vaccine Hesitancy has closely followed the development, implementation, use and evolution of TIP concluding that TIP, with local adaptation, could be a valuable tool for use in all WHO regions, to help address countries' vaccine hesitancy problems. The TIP principles are applicable to communicable, noncommunicable and emergency planning where behavioral decisions influence outcomes.
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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 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".