Delivering sleep: Bed kit distributions to children living in poverty in Tanzania
Bibliographic record
Abstract
Overcrowded housing, insufficient beds, and poor mosquito protection are major barriers to healthy sleep for children living in poverty in developing countries. This study aimed to address these barriers by providing children living in poverty in Tanzania with climate-appropriate items essential for creating a safe and comfortable micro-sleep environment. From 2000–2024, Sleeping Children Around the World (SCAW) delivered 135,500 bed kits to children aged 7–11 years living in poverty in Tanzania. Since 2019, culturally adapted sleep education information was delivered orally to parents of bed kit recipients. Twenty-four distributions reached an average of 5,646 children per cohort years. Mattresses and mosquito nets were consistently included; other items varied. This study demonstrates a community-driven approach to improving the sleep environments of children living in poverty in a developing country, offering a scalable model for promoting sleep health in low-resource settings • Community-driven approach improved sleep environments for Tanzanian children living in poverty • 24 bed kit distributions reached 135,500 children aged 7–11 years • Bed kits provided climate-appropriate items for safe, comfortable sleep spaces • Intervention addressed determinants of poor sleep health in low-resource settings • Scalable model for promoting child sleep health in developing countries
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".