Knowledge, attitude and practice regarding biomedical waste management amongst healthcare workers in a teaching hospital from a north eastern state of India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Bio-medical waste (BMW) means any waste, which is generated during the diagnosis, treatment or immunization of human beings or animals or in research activities or in the production or testing of biological or in any health camp activities. Proper management of BMW ensures protection of public health and environment against any adverse effect associated with such waste materials. Several studies have reported that health care workers lack adequate level of awareness and right attitude regarding proper BMW management which ultimately reflects as incorrect practice of handling and disposal of bio medical waste. This study aimed to assess the knowledge, attitude and practices of healthcare workers regarding bio-medical waste management.Methods: This study was conducted at Tomo Riba Institute of Health and Medical Sciences (TRIHMS), Arunachal Pradesh, India. Hospital based cross sectional study was conducted and questionnaire were administered to 313 healthcare workers of TRIHMS who consented to participate in the study. A predesigned questionnaire for knowledge, attitude and practice study was used for data collection. Data was analysed using Microsoft Excel and STATA 13.Results: Study results show that the average knowledge score was highest amongst nurses (10±2.6) and least in class IV staffs (7.2±1.9). Amongst all participants laboratory technicians were mostly average or poor on the attitude score. Overall only 23 percent (n=73) of the healthcare workers were found to be performing good BMW management practice.Conclusions: Our study revealed that there is significant variation in knowledge, attitude, and practice regarding biomedical waste management among healthcare workers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it