Sterile processing in low- and middle-income countries: an integrative review
Bibliographic record
Abstract
BACKGROUND: Worldwide disparities in surgical capacity are a significant contributor to health inequalities. Safe surgery and infection prevention and control depend on effective sterile processing (SP) of surgical instruments; however, little is known about SP in low- and middle-income countries (LMICs), where surgical site infection is a major cause of postoperative morbidity and mortality. AIM: To appraise and synthesise available evidence on SP in LMICs. METHODS: An integrative review of research literature was conducted on SP in LMICs published between 2010 and 2020. Studies were appraised and synthesised to identify challenges and opportunities in practice and research. RESULTS: Eighteen papers met the inclusion criteria for qualitative analysis. Challenges to advancing SP include limited available evidence, resource constraints and policy-practice gaps. Opportunities for advancing SP include tailored education and mentoring initiatives, emerging partnerships and networks that advance implementation guidelines and promote best practices, identifying innovative approaches to resource constraints, and designing and executing quality assurance and surveillance programmes. DISCUSSION: Research investigating safe surgery, including SP, in LMICs is increasing. Further research and evidence are needed to confirm the generalisability of study findings and effectiveness of strategies to improve SP practice in LMICs. This review will help researchers and stakeholders identify opportunities to contribute. The burdens of unsafe surgery transcend geopolitical borders, and the global surgery and research communities are called upon to negotiate historical and present-day inequities to achieve safe surgery for all.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".