A Web-Based Audio Computer-Assisted Self-interview Application With Illustrated Pictures to Administer a Hepatitis B Survey Among a Myanmar-Born Community in Perth, Australia: Development and User Acceptance Study
Bibliographic record
Abstract
BACKGROUND: Self-administered paper or electronic surveys can create accessibility issues for people with language barriers and limited literacy, whereas face-to-face interviews can create privacy issues and give rise to reporting biases, particularly in the context of sensitive subject matters. An audio computer-assisted self-interview (ACASI) offers an alternative mode of survey administration, and its use has been tested against other survey modes to determine whether the presence of a background narration helps overcome literacy and privacy issues. There are still gaps with the ACASI survey administration because audio narration alone does not assist respondents with limited literacy in choosing response options. To overcome literacy issues, a few studies have used illustrated pictures for a limited number of response options. OBJECTIVE: This study aimed to illustrate all the questions and response options in an ACASI application. This research is part of a larger study comparing different modes of survey administration (ACASI, face-to-face interviews, and self-administered paper surveys) to collect data on hepatitis B knowledge, attitudes, and practices among the Myanmar-born community in Perth, Australia. This study describes the 2-phase process of developing a web-based ACASI application using illustrated pictures. METHODS: The first phase was the preparation of the ACASI elements, such as questionnaire, pictures, brief descriptions of response options, and audio files. Each element was pretested on 20 participants from the target population. The second phase involved synchronizing all the elements into the web-based ACASI application and adapting the application features, in particular, autoplay audio and illustrated pictures. The preprototype survey application was tested for user acceptance on 5 participants from the target population, resulting in minor adjustments to the display and arrangement of response options. RESULTS: After a 12-month development process, the prototype ACASI application with illustrated pictures was fully functional for electronic survey administration and secure data storage and export. CONCLUSIONS: Pretesting each element separately was a useful approach because it saved time to reprogram the application at a later stage. Future studies should also consider the participatory development of pictures and visual design of user interfaces. This picture-assisted ACASI survey administration mode can be further developed and used to collect sensitive information from populations that are usually marginalized because of literacy and language barriers.
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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 | 0.051 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".