Designing tailored interventions to address barriers to vaccination
Bibliographic record
Abstract
Despite efforts to promote vaccination and make vaccination services easily accessible, vaccination coverage rates remain below the target rate for many vaccines in various jurisdictions. The Tailoring Immunization Programmes (TIP) approach was developed by the World Health Organization Regional Office for Europe to support efforts of countries to achieve high and equitable vaccination uptake. In this Canadian Vaccination Evidence Resource and Exchange Centre (CANVax) series, we present key insights from the TIP planning framework to assist vaccine program planners, policy makers and vaccine providers to identify the interventions that will lead to increased vaccine uptake. The TIP is a phased approach that involves the following: 1) a clear diagnosis of the root cause of low vaccination; 2) an intervention based on this understanding; and 3) an evaluation of the implementation process and the impact of the interventions. At the provider-patient level, the approaches and insights of the TIP planning framework could inform vaccination consultation by emphasizing the importance of engaging with and listening to the patients and caregivers, and responding to their needs.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".