Participatory screening of occupational risks at the sterilisation unit of the Niono reference health centre (CSRéf) in 2024
Bibliographic record
Abstract
The assessment of occupational risks represents the initial stage of any occupational health and safety prevention approach. It is essential when planning appropriate preventive actions to protect personnel. Given the widespread increase in occupational hazards in hospital settings, it is crucial to analyze the most exposed work environments. Among these, the sterilization unit stands out due to the frequent handling of contaminated medical devices, the use of chemical products, and the pressure associated with safety and responsiveness requirements. This study was conducted in this context and aimed to assess the specific risks associated with this strategic yet potentially hazardous unit. Moreover, according to the International Labour Organization, each year, 2.78 million workers die because of occupational accidents and work-related diseases. As part of our approach, we adopted the Participatory Risk Screening method (Déparis), which represents the first level of the SOBANE strategy (Screening, Observation, Analysis, and Expertise). The screening was carried out by a nine-member committee coordinated by the Occupational Health and Safety Officer (OHS) of the Referral Health Center (CSRéf). Two Déparis sessions led to the formulation of 50 preventive actions, 66% of which required no financial cost. Six situations (33.33%) were deemed satisfactory, five (50%) moderate, and two (11.11%) unsatisfactory, mainly related to electrical, fire, and psychosocial risks. Twelve aspects require further in-depth analysis. This study highlighted significant occupational risks within the sterilization unit and provides a foundation for improving staff working conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".