A postpartum vaccination promotion intervention using motivational interviewing techniques improves short-term vaccine coverage: PromoVac study
Bibliographic record
Abstract
BACKGROUND: Due to the increasing number of vaccine-hesitant parents, new effective immunization promotion strategies need to be developed to improve the vaccine coverage (VC) of infants. This study aimed to assess the impact of an educational strategy of vaccination promotion based on motivational interviewing (MI) techniques targeting parents and delivered at the maternity ward, for the VC of infants at 3, 5, and 7 months of age. METHODS: An individual educational information session, administered using MI techniques, regarding immunization of infants aged 2, 4, and 6 months was (experimental group) or was not (control group) proposed to parents during the postpartum stay at the maternity ward. Immunization data were obtained through the Eastern Townships Public Health registry for infants at 3, 5, and 7 months of age. Absolute VC increases at 3, 5, and 7 months in the experimental group were calculated and the relative risks with the respective 95% confidence intervals were computed using univariate logistic regression with the generalized estimating equations (GEE) procedure. Multivariate regression using GEE was used to adjust for confounding variables. RESULTS: In the experimental and control groups, 1140 and 1249 newborns were included, respectively. A significant increase in VC of 3.2, 4.9, and 7.3% was observed at 3, 5, and 7 months of age (P < 0.05), respectively. The adjusted relative risk of the intervention's impact on vaccination status at 7 months of age was 1.08 (95% confidence interval: 1.03-1.14) (P = 0.002). CONCLUSIONS: An educational strategy using MI techniques delivered at the maternity ward may be effective in increasing VC of infants at ages 3, 5, and 7 months. MI could be an effective tool to overcome vaccine hesitancy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".