Acceptability and Use of Interactive Voice Response Mobile Phone Surveys for Noncommunicable Disease Behavioral Risk Factor Surveillance in Rural Uganda: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: There is need for more timely data to inform interventions that address the growing noncommunicable disease (NCD) epidemic. With a global increase in mobile phone ownership, mobile phone surveys can bridge this gap. OBJECTIVE: This study aimed to explore the acceptability and use of interactive voice response (IVR) surveys for surveillance of NCD behavioral risk factors in rural Uganda. METHODS: This qualitative study employed user group testing (UGT) with community members. The study was conducted at the Iganga-Mayuge Health and Demographic Surveillance Site (IM-HDSS) in Eastern Uganda. We conducted four UGTs which consisted of different categories of HDSS members: females living in urban areas, males living in urban areas, females living in rural areas, and males living in rural areas. Participants were individually sent an IVR survey, then were brought in for a group discussion using a semistructured guide. Data were analyzed thematically using directed content analysis. RESULTS: Participants perceived that IVR surveys may be useful in promoting confidentiality, saving costs, and raising awareness on NCD behavioral risk factors. Due to the clarity and delivery of questions in the local language, the IVR survey was perceived as easy to use. Community members suggested scheduling surveys on specific days and sending reminders as ways to improve their use for surveillance. Social issues such as domestic violence and perceptions toward unknown calls, technological factors including poor network connections and inability to use phones, and personal issues such as lack of access to phones and use of multiple networks were identified as barriers to the acceptability and use of mobile phone surveys. However, incentives were reported to motivate people to complete the survey. CONCLUSIONS: Community members reflected on contextual and sociological implications of using mobile phones for surveillance of NCD behavioral risk factors. The opportunities and challenges that affect acceptability and use of IVR surveys should be considered in designing and implementing surveillance programs for NCD risk factors.
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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 | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".