Carbapenemase-producing Enterobacteriaceae admission screening practices in Canadian healthcare settings: A cross-sectional survey of respondents working in Canadian hospitals and long-term care facilities (2020)
Bibliographic record
Abstract
Background: Carbapenemase-producing Enterobacteriaceae (CPE) infections are difficult to treat and are associated with high mortality. This study investigated admission screening practices for CPE risk factors within Canadian acute care inpatient hospitals (herein referred to as hospitals) and long-term care facilities (LTCF), and identified perceived barriers to screening, as reported by respondents working in these settings. Awareness of perceived barriers can inform improvements to current screening practices. Methods: An electronic, cross-sectional survey was distributed to a convenience sample consisting of members of the IPAC Canada surveillance and LTCF interest groups, and to the Canadian Nosocomial Infection Surveillance Program’s Carbapenemase-Producing Organisms Workgroup. Recipients with a role in infection prevention and control in a Canadian hospital or LTCF were asked to respond. Survey data were collected from September 7 to December 11, 2020. Descriptive analyses were used to compare the proportion of LTCF and hospital-based respondents who reported that their facility conducted admission screening for CPE risk factors, and to describe perceived barriers to screening. Results: There was a significant difference between respondents from LTCFs and hospitals as to whether screening was performed for CPE (p<0.001), with hospital-based respondents being more likely to report admission screening. Similarly, there was a statistically detectable difference between respondent facility size (based on number of beds) and whether screening was performed (p=0.039), with admission screening reported more frequently by respondents working in facilities with 250-499 beds. Similar barriers to admission screening were identified by LTCF and hospital-based respondents, with both reporting a lack of resources, staffing, and cost as perceived barriers in their facility. Additionally, LTCF-based respondents reported a lack of policies or processes to guide screening. Conclusions: Awareness of specific barriers to admission screening for CPE may help hospitals and LTCFs to improve surveillance practices for CPE colonization and infection to inform prompt implementation of IPAC measures that may limit transmission within healthcare settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".