Vaccination attitudes and mobile readiness: A survey of expectant and new mothers
Bibliographic record
Abstract
Sub-optimal vaccination coverage and recent outbreaks of vaccine-preventable diseases serve as a reminder that vaccine hesitancy remains a concern. ImmunizeCA, a new smartphone app to help track immunizations, may address several reasons for not vaccinating. We conducted a study to describe demographic variables, attitudes, beliefs and information sources regarding pediatric vaccination in a sample of childbearing women who were willing to download an immunization app. We also sought to measure their current mobile usage behaviors and determine if there is an association between participant demographics, attitudes, beliefs and information sources regarding pediatric vaccination and mobile usage. We recruited participants using a combination of passive and active methods at a tertiary care hospital in Ottawa, Canada. We used surveys to collect demographic information, examine attitudes, behavior, and information sources regarding immunization and self-reported mobile phone usage. A total of 54 women participated. The majority had positive attitudes toward vaccination (96%) and intended to vaccinate their children (98%). Participants were interested in information on pediatric vaccination (94%), and found information from public health the most reliable and accessible (78%). Participants also trusted immunization information from their doctor or nurse and public health (83%) more than other sources. There was variability in participant use of mobile apps for other purposes. The median participant mobile readiness score was 3.2. We found no significant associations between participant age, behavior and attitudes regarding vaccination and mobile readiness scores. This is the first evaluation of mobile readiness for a smartphone app to track immunizations. Our findings suggest that there exists an opportunity to provide reliable information on vaccination through mobile devices to better inform the public, however predictors of individual engagement with these technologies merits further study.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".