Factors Contributing to Poor Environmental Hygiene in Kehemu location, Rundu, Namibia
Bibliographic record
Abstract
Solid waste management in Rundu, Namibia, is a major challenge, resulting in significant environmental health hazards. The purpose of this study was therefore to identify and describe the factors contributing to poor environmental hygiene specifically in Kehemu location in Rundu, while the objectives were to explore the factors contributing to poor environmental hygiene in the area. A qualitative approach was employed comprising an explorative and descriptive design. The research population for this particular study consisted of residents of Kehemu location and a sample was drawn from this population using purposive sampling. Data were collected from focus group discussions conducted with 15 (fifteen) residents. The transcribed interviews and narratives from the research notes were organised into codes, main themes and sub-themes. The results from this study revealed, among other things, that the methods used by most households for disposing of waste included digging holes, burning the waste and dumping it in open areas. In addition, factors contributing to poor environmental hygiene in Kehemu location include a lack of dumping sites, dustbins and refuse removal services. The findings of this study call for well-articulated actions to address the factors identified as being associated with poor environmental hygiene in Kehemu. The study recommends that the town council should empower the community by providing dustbins, initiating clean-up campaigns and providing education and awareness-raising as some measures for curbing problems related to environmental health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".