Use of Mobile Forms in Low-Resource Areas for Population Health Surveys: Interview and Field Test Study
Bibliographic record
Abstract
Background: Population health surveys are an important tool to effectively allocate limited resources in low-resource communities. In such an environment, surveys are often done by the local population with pen and paper. Data thus collected are difficult to tabulate and analyze. Objective: The objective of this study was to evaluate the viability and efficiency of mobile forms as an alternative to paper-based surveys in a specific low-resource setting. Methods: We conducted pilot interviews with 53 local surveyors in the Philippines to assess their initial attitudes toward mobile forms. We then built software that can generate mobile forms that are easy to use, capable of working offline, and able to track key metrics such as time to complete questions. Our mobile form was field-tested in 3 locations in the Philippines with 33 surveyors collecting health survey responses from 266 participants. Results: In the pilot phase, we found that 32 out of 53 (60%) of the local surveyors preferred mobile forms over paper. After field-testing, the number of surveyors preferring mobile forms increased to 25 out of 33 (76%) after just using the form a few times. The mobile forms overall demonstrated enhanced efficiency in data collection and usability over paper surveys. Conclusions: Our findings indicate that mobile forms are a viable method to conduct large-scale population health surveys in this low-resource environment.
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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 | 0.032 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".