80 Archetypes of vaccine hesitant caregivers towards COVID-19 immunization during a global pandemic: A qualitative study
Bibliographic record
Abstract
Abstract Primary Subject area Public Health and Preventive Medicine Background As Canada embarks on its rollout of the COVID-19 vaccine, vaccine hesitancy has the potential to hamper success of the vaccination campaign. Multiple surveys show that the number of Canadians willing to take the vaccine is insufficient to achieve herd immunity. Therefore, governments and health agencies are looking for solutions to increase vaccination uptake. Obtaining a better understanding of the perspective of those who are vaccine-hesitant is critical to developing successful implementation strategies for COVID-19 vaccination. Objectives To explore COVID-19 vaccination determinants among hesitant caregivers and describe categories of COVID-19 vaccine hesitancy. Design/Methods We conducted 23 semi-structured telephone interviews with parents recruited from a tertiary pediatric care centre. Seventeen participants had previously attended a specialty clinic to discuss vaccine hesitancy; the remaining were recruited from an infectious diseases follow-up clinic. The interview guide was structured around the Theoretical Domains Framework, assessing 14 behavioural constructs to identify specific determinants that guide behaviour change. Interviews were audio-recorded, transcribed, and analyzed by two independent data coders using a pragmatic inductive approach. Recurring themes were noted among subgroups of participants, who were subsequently divided into categories based on their underlying concerns. Results Five archetypes of vaccine-hesitant caregivers emerged in our data (Table 1). 1). “Bubble Dwellers” perceive themselves to be safe by following public health recommendations, and distinguish themselves from higher-risk groups to whom the vaccine should first be offered. 2). “Worriers and Delayers” identify the pandemic as a threat and are generally supportive of vaccines, but are concerned about side effects and issues surrounding vaccine development and prefer to delay vaccination. 3). “Need-for-Normals” are more concerned about social isolation and the economy than the direct effects of the COVID-19 virus, but express that the idea of a “return to normal” may sway their opinions regarding the vaccine. 4). “Exceptionalists” hold personal misperceptions of vaccine contraindications due to comorbidities or previous experiences with vaccination, and are concerned that the current rollout invokes a “one size fits all” model that does not apply to their circumstances. 5. “Freedom Fighters” view the pandemic as a hoax, are anti-establishment, and believe the information they have been provided is not convincing for them to adopt the vaccine. Conclusion The evolving pandemic provides a unique opportunity to understand determinants of vaccination intention in the vaccine hesitant population. Our qualitative study is unique in that we were able to draw upon pre-identified vaccine hesitant individuals to explore their perspectives around COVID-19 immunization. We propose that rather than viewing these individuals as one homogenous group, policymakers and health professionals address these discrete subgroups with specific communication tools and information. We are hopeful that our results will help tailor implementation strategies that are targeted to different vaccine hesitancy archetypes, as the vaccine is made available to the general public in the coming year.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".