A postpartum intervention for vaccination promotion by midwives using motivational interviews reduces mothers’ vaccine hesitancy, south-eastern France, 2021 to 2022: a randomised controlled trial
Bibliographic record
Abstract
BackgroundDespite childhood vaccine mandates imposed in 2018 in France, parental vaccine hesitancy (VH) remains frequent. Interventions in Quebec, Canada, applying motivational interviewing (MI) techniques have successfully reduced parents' VH for childhood immunisations.AimTo determine whether MI intervention for mothers in maternity wards in the days after birth in France could significantly reduce VH, increase intentions to vaccinate (VI) their child at 2 months and reduce VH social inequalities.MethodsWe conducted a parallel-arm multicentre randomised controlled trial from November 2021 to April 2022 to compare impacts of MI performed by MI-trained midwives (intervention) vs a vaccination leaflet (control). We included 733 mothers from two maternity hospitals in south-eastern France, randomly assigned either arm. The validated Parents Attitudes about Childhood Vaccines questionnaire was used before and after MI or leaflet to assess mothers' VH (13 items, 0-100 score) and VI (1 item, 1-10 score). Difference-in-difference (D-I-D) models were used to estimate net impact of MI vs leaflet for the entire sample and stratified by VH and education level.ResultsMotivational interview intervention reduced mothers' VH score by 33% (p < 0.0001) and increased VI by 8% (p < 0.0001); the effect was largest for the highest initial VH levels. D-I-D analyses estimated net VH decrease at 5.8/100 points (p = 0.007) and net VI increase at 0.6/10 points (p = 0.005). Net VH decrease was highest for high initial VH levels and low education levels.ConclusionsOur results show positive effects of MI intervention, and means of its implementation should be investigated in France.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".