Barriers and facilitators to the use of an immunization application: a qualitative study supplemented with Google Analytics data
Bibliographic record
Abstract
Background: Barriers and facilitators of mobile app adoption are not known. This study examined usage of a new Pan-Canadian immunization app to identify factors that contributed to usage. Methods: Women in their third trimester of pregnancy or had given birth in the previous 3 months were recruited from a hospital obstetrical unit. Fifty-five participants were instructed to download the ImmunizeCA app. After at least 6 months, 10 interviews were conducted, transcribed and coded. Themes identified were compared with aggregate ImmunizeCA usage data (n = 74 212 users). Results: Facilitators included features that address logistical challenges, improved convenience and information access. Barriers included absence of system integration. Concerns regarding the privacy and security of personal health information were not an inhibitor as long as best practices are followed. Google Analytics data on usage supported qualitative findings. Conclusion: Future studies should evaluate the quantitative impact of factors we identified on app uptake and usage. Subsequent mobile app studies may benefit from the use of analytic data as they were found to be effective in helping to validate qualitative data derived from interviews with study participants.
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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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".