Risk perception towards healthcare waste among community people in Kathmandu, Nepal
Bibliographic record
Abstract
BACKGROUND: Healthcare waste management is a serious issue in context of developing countries. Better assessment of both risks and effects of exposure would permit improvements in the management of healthcare waste. However, there is not yet clear understanding of risks, and as consequences, inadequate management practices are often implemented. OBJECTIVES: This study primarily aims to assess risk perception towards healthcare waste and secondly to assess knowledge, attitude and identify the factors associated with risk perception. RESULTS: A cross-sectional community based study was carried out among 270 respondents selected through multistage sampling technique. Face-to-face interview was conducted using semi-structured questionnaires. Risk perception was classified as good and poor based on mean score. Bivariate and multivariate analyses were carried out to determine the associates of risk perception. More than half, 52% of the sampled population had a poor risk perception towards healthcare waste. More than a quarter 26.3% had inadequate knowledge and forty percent (40%) had a negative attitude towards health care waste management. Having knowledge (OR = 3.31; CI = 1.67-6.58) was a strong predictor of risk perception towards healthcare waste. The perception of risk towards healthcare waste among community people was poor. This highlights the need for extensive awareness programs. Promoting knowledge on healthcare waste is a way to change the perception in Nepal. Community engaged research approach is needed to address environmental health concerns among public residents.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".