Improving sterile processing practices in Cambodian healthcare facilities
Bibliographic record
Abstract
BACKGROUND: Sterile processing practices in low-resource countries contribute to greater post-operative infection rates compared to high-resource countries. Provision of a sterile processing training program in Tanzania and Ethiopia demonstrated statistically significant improvements in sterile processing practice, a key requisite for safe surgical care. AIM: To determine if a sterile processing program in a South East Asia country would result in improved conditions and practice in urban and rural healthcare facilities. METHODS: In 2019, a mixed-methods study was conducted with two cohorts in Cambodia, involving a total of eight healthcare facilities and 43 healthcare workers. Quantitative data were collected using a sterile processing assessment tool and a multiple-choice test pre- and post-training. Qualitative data in the form of interviews were obtained several months post-training. FINDINGS: Test results showed statistically significant and sustained effect of training over a four-six month period, as well as a large positive effect on SP knowledge in both cohorts. Analysis of hospital assessment data revealed an aggregate improvement of 36% in sterile processing benchmarks. While all participants reported increased knowledge and confidence (quantitative), rural participants conveyed a lack of support (qualitative) to implement practice changes. CONCLUSION: The training course produced improvements in both rural and urban facilities. Findings highlight the importance of informing administrators of the rationale for needed improvements, ensuring funding is available to implement recommendations, and for governments to hold administrators accountable for improvements aligning with universally recommended sterile processing standards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".