Mixed methods evaluation of the impact of a short term training program on sterile processing knowledge, practice, and attitude in three hospitals in Benin
Bibliographic record
Abstract
Background: Proper sterile processing is fundamental to safe surgical practice and optimal patient outcomes. Sterile processing practices in low and middle-income countries often fall short of recommended standards. The impact of education and training on sterile processing practices in low and middle-income countries is unknown. We designed a sterile processing education course, including mentoring, and aimed to evaluate the impact on participants' personal knowledge, skills, and practices. We also aimed to identify institutional changes in sterile processing practices at participants' work places. Methods: A mixed methods design study was conducted using a Hospital Sterile Processing Assessment Tool, knowledge tests, and open-ended interviews. Results: Education and mentoring improved how workers understood and approached their work and to what they paid attention. Sterile processing workers were also better able to identify resources available to do their work and showed improved understanding of the impact of their work on patient safety. Conclusions: Health care organizations seeking to improve surgical outcomes can find easy wins requiring minimal cost expenditures by paying attention to sterile processing practices. Investing in education and low-cost resources, such as cleaning detergents and brushes, must be part of any quality improvement initiative aimed at providing safe surgery in low and middle-income countries.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".