The impact of a short-term training program on workers’ sterile processing knowledge and practices in 12 Ethiopian hospitals: A mixed methods study
Bibliographic record
Abstract
BACKGROUND: The need for increased attention to surgical safety in low- and middle-income countries invited organizations worldwide to support improvements in surgical care. However, little is written about issues in instrument sterilization in low- and middle-income countries including Ethiopia. OBJECTIVE: The study aims to identify the impact of a sterile processing course, with a training-of-trainers component and workplace mentoring on surgical instrument cleaning and sterilization practices at 12 hospitals in Ethiopia. METHOD: A mixed-methods research design that incorporates both qualitative and quantitative research approaches to address issues in sterile processing was used for this study. The quantitative data (test results) were validated by qualitative data (hospital assessments, including observations and participant feedback). Twelve hospitals were involved in the training, including two university teaching hospitals from two regions of Ethiopia. In each of the two regions 30 sterile processing staff were invited to participate in a three-day course including theory and skills training; 12-15 of these individuals were invited to remain for a two-day training of trainers course. The collected quantitative data were analysed using a paired t-test by SPSS software, whereas comparative analysis was employed for the qualitative data. RESULTS: Process, structural, and knowledge changes were identified following program implementation. Knowledge test results indicated an increase of greater than 20% in participant sterile processing knowledge. Changes in process included improved flow of instruments from dirty to clean, greater attention to detail during the cleaning and decontamination steps, more focused inspection of instruments and careful packaging, as well as changes to how instruments were stored. Those trained to be trainers had taught over 250 additional staff. CONCLUSIONS: Increased attention to and knowledge in sterile processing practices and care of instruments with a short, one-week course provides evidence that a small amount of resources applied to a largely under-resourced area of healthcare can result in decreased risks to patients and staff. Providing education in sterile processing and ensuring staff have the ability to disseminate their learnings to other health care providers results in decreasing risks of hospital associated infections in patients.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".