Status of the Prevention of Multidrug-Resistant Organisms in International Settings: A Survey of the Society for Healthcare Epidemiology of America Research Network
Bibliographic record
Abstract
OBJECTIVE To examine self-reported practices and policies to reduce infection and transmission of multidrug-resistant organisms (MDRO) in healthcare settings outside the United States. DESIGN Cross-sectional survey. PARTICIPANTS International members of the Society for Healthcare Epidemiology of America (SHEA) Research Network. METHODS Electronic survey of infection control and prevention practices, capabilities, and barriers outside the United States and Canada. Participants were stratified according to their country's economic development status as defined by the World Bank as low-income, lower-middle-income, upper-middle-income, and high-income. RESULTS A total of 76 respondents (33%) of 229 SHEA members outside the United States and Canada completed the survey questionnaire, representing 30 countries. Forty (53%) were high-, 33 (43%) were middle-, and 1 (1%) was a low-income country. Country data were missing for 2 respondents (3%). Of the 76 respondents, 64 (84%) reported having a formal or informal antibiotic stewardship program at their institution. High-income countries were more likely than middle-income countries to have existing MDRO policies (39/64 [61%] vs 25/64 [39%], P=.003) and to place patients with MDRO in contact precautions (40/72 [56%] vs 31/72 [44%], P=.05). Major barriers to preventing MDRO transmission included constrained resources (infrastructure, supplies, and trained staff) and challenges in changing provider behavior. CONCLUSIONS In this survey, a substantial proportion of institutions reported encountering barriers to implementing key MDRO prevention strategies. Interventions to address capacity building internationally are urgently needed. Data on the infection prevention practices of low income countries are needed. Infect Control Hosp Epidemiol. 2016:1-8.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".