WhatsApp-Based Focus Groups Among Mexican-Origin Women in Zika Risk Area: Feasibility, Acceptability, and Data Quality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Despite unprecedented advances in worldwide access to the internet via smartphones, barriers to engaging hard-to-reach populations remain in many methods of health research. A potential avenue for conducting qualitative research is via participatory web-based media, including the free, popular social platform WhatsApp. However, despite the clear advantages of engaging with participants over a well-established web-based platform, logistical challenges remain. OBJECTIVE: This study aims to report evidence on the feasibility and acceptability of WhatsApp as a method to conduct focus groups. METHODS: A pilot focus group was conducted with Spanish-speaking women near the US-Mexico border. The content focus was knowledge and perceived risks for exposure to the Zika virus during pregnancy. RESULTS: Evidence was obtained regarding WhatsApp as a low-cost, logistically feasible methodology that resulted in rich qualitative data from a population that is often reticent to engage in traditional research. A total of 5 participants participated in a focus group, of whom all 5 consistently contributed to the focus group chat in WhatsApp, which was conducted over 3 consecutive days. CONCLUSIONS: The findings are noteworthy at a time when face-to-face focus groups, the gold standard, are risky or precluded by safe COVID-19 guidelines. Other implications include more applications and evaluations of WhatsApp for delivering one-on-one or group health education interventions on sensitive topics. This paper outlines the key steps and considerations for the replication or adaptation of methods.
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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 | 0.067 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it