User Perceptions and Experiences of an Interactive Voice Response Mobile Phone Survey Pilot in Uganda: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: With the growing burden of noncommunicable diseases in low- and middle- income countries, the World Health Organization recommended a stepwise approach of surveillance for noncommunicable diseases. This is expensive to conduct on a frequent basis and using interactive voice response mobile phone surveys has been put forth as an alternative. However, there is limited evidence on how to design and deliver interactive voice response calls that are robust and acceptable to respondents. OBJECTIVE: This study aimed to explore user perceptions and experiences of receiving and responding to an interactive voice response call in Uganda in order to adapt and refine the instrument prior to national deployment. METHODS: A qualitative study design was used and comprised a locally translated audiorecorded interactive voice response survey delivered in 4 languages to 59 purposively selected participants' mobile phones in 5 survey rounds guided by data saturation. The interactive voice response survey had modules on sociodemographic characteristics, physical activity, fruit and vegetable consumption, diabetes, and hypertension. After the interactive voice response survey, study staff called participants back and used a semistructured interview to collect information on the participant's perceptions of interactive voice response call audibility, instruction clarity, interview pace, language courtesy and appropriateness, the validity of questions, and the lottery incentive. Descriptive statistics were used for the interactive voice response survey, while a framework analysis was used to analyze qualitative data. RESULTS: Key findings that favored interactive voice response survey participation or completion included preference for brief surveys of 10 minutes or shorter, preference for evening calls between 6 PM and 10 PM, preference for courteous language, and favorable perceptions of the lottery-type incentive. While key findings curtailing participation were suspicion about the caller's identity, unclear voice, confusing skip patterns, difficulty with the phone interface such as for selecting inappropriate digits for both ordinary and smartphones, and poor network connectivity for remote and rural participants. CONCLUSIONS: Interactive voice response surveys should be as brief as possible and considerate of local preferences to increase completion rates. Caller credibility needs to be enhanced through either masking the caller or prior community mobilization. There is need to evaluate the preferred timing of interactive voice response calls, as the finding of evening call preference is inconclusive and might be contextual.
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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 | 0.095 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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; both teacher heads agree on what is shown here.
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".