Remote Moderated Usability Testing of a Mobile Phone App for Remote Monitoring of Pregnant Women at High Risk of Preeclampsia in Karachi, Pakistan
Bibliographic record
Abstract
This study assessed the usability of the smartphone app, named “Raabta” from the perspective of pregnant women at high risk of preeclampsia to improve the Raabta app for future implementation. Think-aloud and task-completion techniques were used with a purposive sample of 14 pregnant women at high risk of preeclampsia. The sessions were audio-recorded and later professionally transcribed for thematic analysis. The study generated learnings associated with four themes: improving the clarity of instructions, messaging, and terminology; accessibility for non-tech savvy and illiterate Urdu users; enhancing visuals and icons for user engagement; and simplifying navigation and functionality. Overall, user feedback emphasized the importance of enhancing the clarity of instructions, messaging, and terminology within the Raabta app. Voice messages and visuals were valued by users, particularly among the non-tech savvy and illiterate Urdu users, as they enhance accessibility and enable independent monitoring. Suggestions were made to enhance user engagement through visual improvements such as enhanced graphics and culturally aligned color schemes. Lastly, users highlighted the need for improved navigation both between screens and within screens to enhance the overall user experience. The Raabta app prototype will be modified based on the feedback of the users to address the unique needs of diverse groups.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".