The Accra School Health and Environment Study (ASHES): A study of the urban environment and child health and development in Accra
Bibliographic record
Abstract
Abstract Elementary school and early education are crucial for children’s cognitive and social development, as well as lifetime health and well-being. For children in cities, urban schools present numerous advantages in education quality and access to resources and opportunities that stimulate learning and improve health. In Sub-Saharan African (SSA) cities, the complexity of the urban environment requires careful consideration of school environments in enhancing child health and development. Yet, little is known about environmental conditions in schools and schoolchildren’s health in rapidly urbanizing SSA cities. This paper describes the various datasets captured within the Accra School Health and Environment Study (ASHES), a study platform designed to characterize air and noise pollution at elementary schools and for schoolchildren, and their influence on key markers of childhood health and development. We outline environmental exposures and health and developmental outcomes among children living in a major metropolitan area in SSA, along with preliminary results and planned analyses. ASHES was implemented in Accra, one of the fastest growing metropolises in SSA. Between July 2022 and May 2023, 1,037 children (∼60% female) aged 8-12 were recruited from 90 public (74%) and private primary schools. Weeklong fine particulate matter (PM 2.5 ), black carbon (BC), and sound pressure levels were measured in the schoolyards. Homes of the children were geocoded and linked with spatial prediction models to estimate ambient pollutant concentrations at each child’s residence. Data were also captured on anthropometry, blood pressure, respiratory function, cognitive and behavioral functions, and sleep quality. Questionnaires gathered additional information on school, household, and sociodemographic factors. Preliminary results suggest that a third of children were hypertensive, 30% were overweight or obese, and 14% had behavioral problems. PM 2.5 and noise levels across schools exceeded local and international standards. Several ongoing epidemiologic analyses will examine the key exposures in relation to the major outcomes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".