Level of involvement of patients and accompanying persons in the management of biomedical waste in Benin
Bibliographic record
Abstract
Introduction: The Management of Biomedical Waste (MBW), needs the involvement and the action of professional or social entities in relationship with health facilities. This study aims to assess the involvement level of Patients and their Accompanying persons (PA) in the MBW. Methods: It was a cross-sectional and analytic study made on 409 PA hired by their commodities, in six health facilities in Benin. Data were collected through a survey on sociodemographic characteristics, type of waste, knowledge and perception of PA on the MBW, the access to storage places and perceptions on health and environment in link with the MBW. The data were entered and treated by Epidata and the software R 4.1.1.1. The proportions were compared with the Chi-square test. Results: At the univariate analysis, the health facility, the gender and the knowledge of the storage place were associated to the involvement of PA in the MBW. The access to the storage places were associated to the health facility, the profession and the knowledge of storage places. The PA that knew the storage place of BW were more than seven times at risk of been involved in the MBW compared to the others. Conclusion: 26.9 % of PA have access to the storage place of BW. Regarding the potential infectious risk that could induct this practice for PA, it is necessary to improve the management system of BW and sensitize the PA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".