Can mobile technologies improve on-time vaccination? A study piloting maternal use of ImmunizeCA, a Pan-Canadian immunization app
Bibliographic record
Abstract
Mobile applications have the potential to influence vaccination behavior, including on-time vaccination. We sought to determine whether the use of a mobile immunization app was associated with the likelihood of reporting on-time vaccination in a cohort of 50 childbearing women. In this pilot study, we describe participant reported app use, knowledge, attitudes or beliefs regarding pediatric vaccination and technology readiness index (TRI) scores. To explore if app use is associated with change in attitudes, beliefs or behavior, participants were instructed complete a baseline survey at recruitment then download the app. A follow up survey followed 6-months later, reexamining concepts from the first survey as well as collecting participant TRI scores. Changes in Likert scores between pre and post survey questions were compared and multivariate logistic regression was used to assess the relationship between TRI score and select survey responses. Thirty-two percent of participants perceived that the app made them more likely to vaccinate on time. We found some individuals' attitudes toward vaccines improved, some became less supportive and in others there was no change. The mean participant TRI score was 3.25(IQR 0.78) out of a maximum score of 5, indicating a moderate level of technological adoption among the study cohort population. While the app was well received, these preliminary results showed participant attitudes toward vaccination moved dichotomously. Barriers to adoption remain in both usability and accessibility of mobile solutions, which are in part dependent on the user's innate characteristics such as technology readiness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".