822 Swimsafe – child survival swimming skills – risks management
Bibliographic record
Abstract
Background Child drowning in LMICs has become a major new public health issue. Drowning is the leading cause of death in children 1–17 yrs in Bangladesh. The lifecycle approach addresses child drowning for each age group. The BASS program is a multi-intervention drowning prevention program in rural Bangladesh demonstrating sustainable, effective interventions that can be scaled up. Methods Community-based Participatory Research was done in a rural community under injury surveillance. The partners in the research are UBC, CIPRB, TASC and RLSSA. SwimSafe is one of a suite of interventions. Risks of the SwimSafe intervention have been carefully reviewed and mitigated. Results There is risk assessment and mitigation in the planning and implementation of SwimSafe. The risks are greater when SwimSafe is provided in village ponds. Village pond risk management is also needed to ensure that the pond is not an add source of risk. Consultation with experts within the RLSSA is ongoing. There is Community Swimming Instructor (CSI) Training and policy on water instructor to child ratios, and other risk management issues. There have been no BASS SwimSafe training significant injuries or drowning events. SwimSafe was provided for 1393 children 3–9 yrs. 73% met the SwimSafe competency. Pass rates are not acceptable until age 6. There is increased safety risk with children under 6 yrs. SwimSafe for children with disabilities is possible but the impact remains unclear. Conclusions Risk management is a very important part the SwimSafe intervention in pond structures. Training and policy for CSI’s and supervision for enforcement is needed to prevent unintended injury or mortality. The risks are greater in children under the age of 6. The ratio of CSI to learners and the careful management of the in water teaching sessions is necessary. SwimSafe training for children under 6 yrs of age, is recommended only in research settings with high risk management ability. Interventions for children with disabilities remains a challenge.
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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 | 0.003 |
| 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 | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.011 |
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".