Does teaching children to swim increase exposure to water or risk-taking when in the water? Emerging evidence from Bangladesh
Bibliographic record
Abstract
BACKGROUND: SwimSafe, a basic swimming and safe rescue curriculum, has been taught to large numbers of children in Bangladesh. Teaching swimming potentially increases risk if it increases water exposure or high-risk practices in water. This study compares water exposure and risk practices for SwimSafe graduates (SS) with children who learned swimming naturally. METHODS: Interviewers obtained detailed water exposure histories for the preceding 48 h from 3936 SS aged 6-14 and 3952 age-matched and sex-matched children who had learned swimming naturally. Frequencies of water exposure and water entries for swimming or playing were compared. RESULTS: There were 9741 entries into water among the 7046 participants in the 48 h prior to interview. About one-third (31.2%) had no water entries, one-tenth (10.5%) entered once, half (49.2%) entered twice and a tenth (9.1%) entered three or more times. Proportions of children in each group were similar. About 99.5% of both groups only entered the water for bathing. For those entering to swim or play, the mean number of entries was similar (SS 1.63, natural swimmer (NS) 1.36, p=0.40). Swimming or playing alone in the water was rare (1 SS, 0 NS). CONCLUSIONS: Most water exposure for children is for bathing. Less than 1% swam or played in the water during the 48 h recall period (0.6% SS, 0.4% NS). Learning swimming in SwimSafe did not increase water exposure nor did it increase water entry for playing or swimming compared with children who learned to swim naturally.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".