Motivational interviewing: A powerful tool to address vaccine hesitancy
Bibliographic record
Abstract
According to the World Health Organization, vaccine hesitancy is among the top threats to global health and few effective strategies address this growing problem. In Canada, approximatively 20% of parents/caregivers are concerned about their children receiving vaccines. Trying to convince them by simply providing the facts about vaccination may backfire and make parents/caregivers even more hesitant. In this context, how can health care providers overcome the challenge of parental decision-making needs regarding vaccination of their children? Motivational interviewing aims to support decision making by eliciting and strengthening a person's motivation to change their behaviour based on their own arguments for change. This approach is based on three main components: the spirit to cultivate a culture of partnership and compassion; the processes to foster engagement in the relationship and focus the discussion on the target of change; and the skills that enable health care providers to understand and address the parent/caregiver's real concerns. With regard to immunization, the motivational interviewing approach aims to inform parents/caregivers about vaccinations, according to their specific needs and their individual level of knowledge, with respectful acceptance of their beliefs. The use of motivational interviewing calls for a respectful and empathetic discussion of vaccination and helps to build a strong relationship. Numerous studies in Canada, including multicentre randomized controlled trials, have proven the effectiveness of the motivational interviewing approach. Since 2018, the PromoVac strategy, an educational intervention based on the motivational interviewing approach, has been implemented as a new practice of care in maternity wards across the province of Quebec through the Entretien Motivationnel en Maternité pour l'Immunisation des Enfants (EMMIE) program.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".