Knowledge, Attitude & Practice (KAP) among Staff Nurses Regarding Biomedical Waste Management (BMW): A Correlational Study Design
Bibliographic record
Abstract
Background: The safe and effective management of biomedical waste generated in the hospital is a collective responsibility of all health workers involved in patient care. It is cardinal that the staff nurses must have up to date knowledge regarding handling and management of biomedical waste for the sake of both themselves and the patients. The aim of the study was to assess the knowledge, attitude and practice levels among staff nurses regarding biomedical waste management and to determine the relationship between knowledge, attitude and practice levels. Materials and Methods: The following study adopted a Correlational design. 100 staff nurses from selected hospitals in Kollam, Kerala were assigned using convenience sampling. The data was collected using a structured knowledge questionnaire, a five-point Likert attitude scale and a verbal response checklist. Results: Findings of the study revealed that the Spearman’s Rank Coefficient (Rho) ‘ρ’ calculated at 0.65** for finding the relationship between knowledge and attitude levels was statistically significant at P<0.01 level. Also, the ‘ρ’ value (0.59**) calculated between knowledge and practice levels was significant at 0.01 level. The Chi square (χ2) value calculated was statistically significant for Knowledge level and selected sociodemographic variables such as; Gender (χ2 18.56**, df=02, P<0.01 level) and Professional Experience (χ2 11.59*, df-04, p<0.05 level); Also, between professional experience and Attitude (6.44**) and professional experience and practice levels (10.17**). The Fisher’s exact test revealed association between Gender and Attitude (P# 0.016*, df=02, P<0.05 level) as well as Gender and Practice (P# 0.005**, df-01, P<0.01 level of significance). Conclusion: The findings confirmed that there was a medium/moderate positive correlation between knowledge and attitude as well as knowledge and practice levels regarding biomedical waste management among staff nurses. Keywords: KAP, BMW, Staff Nurses, Correlational Design.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".