Awareness Regarding Safe Drinking Water among People Living in Rural Area of Bangladesh
Bibliographic record
Abstract
This cross-sectional, descriptive study was conducted in a rural area of West Bhagalpur village under Bajitpur upazila of Kishoreganj district, Bangladesh, from July 2023 to June 2024, to determine the awareness regarding particular aspects of safe drinking water among the rural adults. A convenient non-probability sampling was adopted. A total of 125 participants were interviewed based on a semi-structured questionnaire. Out of 125 respondents, 44(35.2%) were male and 81(64.8%) were female; all were aged between 18 and 65 years. Regarding qualities for safe drinking water, most of them said 82(65.6%) colorless, odorless, free from turbidity, tasteless and other said 24(19.2%). The majority 103(82.4%) had shallow tube well, 14(11.2%) had deep tube well, 7(5.6%) had tap water, only 1 used pond water. Most of them were taking drinking water without purification direct from tube well 88(70.4%), followed by boiling and filters 16(12.8%) and rest of them purify their drinking water by disinfection 5(4%). For improvement of water supply, most of them recommended 23(46.93%) to provide subsidized filters, create awareness regarding hazards of impure water drinking and built more tube well and followed by built more tube wells 16(32.65%), create awareness 6(12.24%) and provide subsidized filters 4(8.16%). For storage of water most of them used jug 86(68.8%), followed by bucket 25(20%), mud pot 8(6.4%) and only 6(4.8%) used others containers. 121(96.8%) had no water-borne disease in the last 6 months, while 4(3.2%) had diarrhoea. Surprisingly, most of them said yes to store water for future use 84(67.2%), while 41(32.8%) declined. This study presented a comprehensive overview of the awareness regarding particular aspects of safe drinking water among people living in rural areas of Bangladesh. CBMJ 2025 July: vol. 14 no. 02 P:149-153
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".