Women’s Perceptions of Using Mobile Phones for Maternal and Child Health Support in Afghanistan: Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Growing rates of global mobile subscriptions pave the way for implementation of mobile health (mHealth) initiatives, especially among hard-to-reach populations. OBJECTIVE: This study aimed to determine the perceptions of Afghan women regarding the use of mobile phones for maternal and child health services. METHODS: A cross-sectional survey was conducted in both rural and urban districts of Nangarhar Province, Afghanistan. The interviewer-administered questionnaire was used to assess participants' demographic profile, mobile phone usage, and perception of respondents toward different aspects of health care delivery via mobile phones. RESULTS: Of the 240 participants, 142 (59.2%) owned mobile phones and 220 (91.7%) routinely used mobile phones. Approximately 209 (87.1%) of participants were willing to receive health messages via a mobile phone. Automated voice call was the most preferred method for sending health messages. More than 90% of the women reported that they would like to receive reminders for their children's vaccinations and antenatal care visits. CONCLUSIONS: Users' perception was associated with mobile phone ownership, literacy level, and experience using mobile phones. In the study area, where the literacy rate is low, mHealth was well perceived.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".