The reality of patient bodily waste management: Nurse perceptions of current practice & staff safety
Bibliographic record
Abstract
Objective: The purpose of this study was to evaluate nurse knowledge about the risks of exposure to patient bodily waste, nurse perceptions about procedures and reporting, and current levels of satisfaction with how risks of exposure to patient waste are managed. Patient bodily waste management impacts healthcare workers and healthcare organizations. For nurses and other healthcare workers, the risk of exposure to pathogens can have adverse health effects, increase stress, and reduce satisfaction with their job, potentially leading to issues related to retention. Evidence suggests that proper training and using devices to reduce exposure risks and improve shorter bedside toileting, may reduce stress, and improve work satisfaction. Reducing risk of increased healthcare associated infections of patients and healthcare workers may have a positive impact on the organization with reduced cost of care.Methods: A survey focused on nurses’ knowledge about their risk of exposure, nurse understanding of procedures and incident reporting, and morale and satisfaction with their job was conducted. Results. The findings suggest that there were conflicting responses related to the acknowledgement of risk, reporting incidents, and the use of personal protective equipment.Results: The findings suggest that there were conflicting responses related to the acknowledgement of risk, reporting incidents, and the use of personal protective equipment.Conclusions: Organizations benefit from addressing these concerns to improve morale and satisfaction, nurse retention, healthcare worker dignity, and the quality of patient care.
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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 | 0.009 | 0.026 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".