SMS Text Messages for Parents for the Prevention of Child Drowning in Bangladesh: Acceptability Study
Bibliographic record
Abstract
BACKGROUND: In many cases, greater use is being made of mobile phone text messages as a means of communication between patients and health care providers in countries around the world. OBJECTIVE: We studied the use of mobile phones and the factors related to the acceptability of text messages for parents for the prevention of child drowning in Bangladesh. METHODS: From a randomized controlled trial involving 800 parents, 10% (80/800) were selected, and socioeconomic status, mobile phone use, and acceptability of SMS text messages for drowning prevention were measured. Participants with at least one child under 5 years of age were selected from rural areas in Rajshahi District in Bangladesh. Mobile phone-based SMS text messages were sent to the participants. Multivariate regression was used to determine the factors related to the acceptability of text messages for the prevention of child drowning in Bangladesh. RESULTS: The acceptability of SMS text messages for the prevention of child drowning in Bangladesh was significantly lower among women (odds ratio [OR] 0.50, 95% CI 0.12-1.96, P=.02) than among men, lower for parents older than 30 years (OR 0.17, 95% CI 0.14-1.70, P=.01) compared to parents younger than 30 years, higher among parents who had an education (OR 1.63, 95% CI 1.11-5.80, P=.04) than among illiterate parents, and higher among parents with a monthly household income over 7000 Bangladeshi Taka (approximately US $82.54; OR 1.27, 95% CI 1.06-1.96, P=.05) than among parents whose monthly income was less than 7000 Bangladeshi Taka. CONCLUSIONS: The high percentage of mobile phone use and the acceptability of SMS text messages for parents for the prevention of child drowning are encouraging, in terms of identifying the best strategy for using such technologies, and deserve further evaluation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".