Investigating Knowledge, Attitude and Health Care Waste Management by Health Workers in a Nigerian Tertiary Health Institution
Bibliographic record
Abstract
INTRODUCTION: Inadequate knowledge and practice of health care waste management by health workers may have serious health consequences and a significant impact on the environment.OBJECTIVE: The purpose of the study was to ascertain the knowledge, attitude and practice of hospital waste management among health workers in Enugu.METHODS: A cross sectional descriptive survey was carried out among 115 health workers at the University of Nigeria Teaching Hospital Enugu. Data were collected using self-administered questionnaire, and was analysed using SPSS version 21. Statistical significance of association between variables was assessed using Chi-square test at p<0.05. Ethical clearance was obtained from the Research Ethics Committee of UNTHRESULTS: All 115 respondents returned the completed questionnaires. Sixty (52.2%) were females and fifty five (47.8%) were males. The mean age of respondents was 31.7 ±11.8 years. Ninety three (80.9%) had heard of hospital waste management, 95 (83%) were aware that hospital waste is classified into hazardous and non-hazardous waste. Ninety nine (86.1%) were aware of waste segregation, only 25(21.7%) dispose medical waste in specified color coded container always. Majority 90 (78.3%) use latex gloves when handling waste.CONCLUSION: Most of the respondents knew what health care waste management means (HCWM), but very few practiced appropriate health care waste management. Health education and training is recommended for the health care workers periodically and regularly.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".