Comfort or hesitancy: A cross-sectional study of modifiable factors associated with co-vaccination behavior among United States and Canadian adults
Bibliographic record
Abstract
Objective: Co-vaccination, or receiving multiple vaccines at once, may improve vaccination uptake and reduce missed opportunities to vaccinate. Although generally considered safe and effective, co-vaccination is not well accepted outside of travel and childhood immunization. Myriad psychological, physical and social influences affect vaccination decisions, but limited work has explored modifiable factors associated with co-vaccination comfort. Identifying such factors may better inform interventions targeting co-vaccination. Methods: This cross-sectional study examined how capability, opportunity, and motivation (COM-B model) relate to co-vaccination comfort among United States (U.S.; N = 604) and Canadian ( N = 586) adults in January 2024. Index variables representing capability, opportunity, and motivation were constructed from survey items. Linear regression models were used to assess independent associations of capability, opportunity, and motivation with co-vaccination comfort. Results: All factors were positively associated with co-vaccination comfort in both samples. Capability was the strongest predictor (U.S.: β = 4.82, 95 % CI [4.24, 5.4]; Canada: β = 4.28, 95 % CI [3.7, 4.88]), followed by opportunity (U.S.: β = 4.1, 95 % CI [3.6, 4.6]; Canada: β = 4.1, 95 % CI [3.61, 4.59]), and motivation (U.S.: β = 2.94, 95 % CI [2.61, 3.27]; Canada: β = 2.57, [2.24, 2.9]). Conclusions: This study leveraged the COM-B model to identify behavioral factors associated with co-vaccination comfort. Future experiments should examine whether manipulating these factors impacts co-vaccination decisions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".