Improving WASH facilities and practices in Bangladeshi schools: progress and challenges from 2014 to 2018
Bibliographic record
Abstract
BACKGROUND: In low- and middle-income countries like Bangladesh, inadequate water, sanitation, and hygiene (WASH) practices lead to a higher disease burden among children and hinder their academic performance. However, there have been efforts to improve WASH between 2014 and 2018. OBJECTIVES: The study aimed to investigate changes in WASH facilities and practices in Bangladeshi schools from 2014 to 2018. METHODS: We analyzed pooled data from Bangladesh National Hygiene Survey 2014 and 2018. We performed descriptive analysis, bivariate analysis, and multivariate Generalized Estimating Equation (GEE) to analyze the changes over the four years time period. RESULTS: Results showed that basic drinking water services increased from 78% in 2014 to 90% in 2018. Schools showed a significant increase in basic sanitation services from 19% in 2014 to 52% in 2018. We discovered that students' access to water and soap increased from 2014 to 2018, from 21% to 35%. In the GEE model, we found that change in time, non govt urban schools were associated factors with improved basic drinking water services. For basic sanitation services, changes in time, school type and area type were significantly associated higher services. And for basic hygiene services, the associated factors were: schools having hygiene promotion visits, and availability of hygiene brigades at schools managed by students. CONCLUSION: WASH services in Bangladeshi schools have improved significantly, yet disparities exist, particularly in government and rural schools. Although students' knowledge improved, their practices still need improvements through training on proper WASH practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".