From vaccine hesitancy to vaccine motivation: A motivational interviewing based approach to vaccine counselling
Bibliographic record
Abstract
The COVID-19 pandemic highlighted Vaccine Hesitancy (VH) as an accelerating global phenomenon that must be addressed. According to the WHO, thirty to fifty percent of the world’s population are VH. Motivational Interviewing (MI) is an evidence-based communication style demonstrated to significantly reduce VH. MI guides people toward change through the expression of empathy and by respecting an individual’s autonomy. Healthcare providers (HCPs) are the primary implementors of vaccine policies and the most trusted advisors and influencers of vaccination intention at the individual patient level. Training HCPs in MI is one of the most effective strategies to overcome VH. Many countries are currently implementing HCP training programs and population-based MI interventions to improve vaccine uptake. MI conversations are ‘the heart’ of vaccine decision-making processes. Understanding individual patient-level drivers of hesitancy allows clinicians to efficiently provide tailored, accurate information that reinforces a person’s own motivation and confidence in their own decision. This paper describes a 4-step practical framework designed to support HCPs in their dialogue with vaccine-hesitant patients. (1) Engaging to establish a trustful relationship and safety to freely express opinions, beliefs, and knowledge gaps; (2) Understanding what matters most to the individual; (3) Offering Information to co-build accurate knowledge in order to guide the individual toward vaccine intention (4) Clarifying and Accepting to validate an individual’s decision-making autonomy. We believe that our pragmatic approach can contribute to greater acceptability of COVID-19 and other vaccines, and enable rapid deployment of practical MI skills across care systems.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".