Motivational interview-based health mediator interventions increase intent to vaccinate among disadvantaged individuals
Bibliographic record
Abstract
Coverage for recommended COVID-19 and diphtheria-tetanus-poliomyelitis (DTP) booster shots is often inadequate, especially among disadvantaged populations. To help health mediators (HMs) involved in outreach programs deal with the problems of vaccine hesitancy (VH) in these groups, we trained them in motivational interviewing (MI). We evaluated the effectiveness of this training among HMs on their MI knowledge and skills (objective 1) and among the interviewees on their vaccination readiness (VR) and intention to get vaccinated or accept a booster against COVID-19 and/or DTP (objective 2). Two MI specialists trained 16 HMs in a two-day workshop in May 2022. The validated MISI questionnaire evaluated HMs' acquisition of MI knowledge and skills (objective 1). Trained HMs offered an MI-based intervention on vaccination to people in disadvantaged neighborhoods of Marseille (France). Those who consented completed a questionnaire before and after the interview to measure VR with the 7C scale and intentions regarding vaccination/booster against COVID-19 and DTP (objective 2). The training resulted in HMs acquiring good MI skills (knowledge, application, self-confidence in using it). HMs enrolled 324 interviewees, 96% of whom completed both questionnaires. VR increased by 6%, and intentions to get vaccinated or update COVID-19 and DTP vaccination increased by 74% and 52% respectively. Nearly all interviewees were very satisfied with the interview, although 21% still had questions about vaccination. HMs assimilated MI principles well. MI use in outreach programs appears to show promise in improving vaccine confidence and intentions among disadvantaged people.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".