Determinants of Adherence with Antimicrobial Resistant Organism (ARO) Admission Screening in a Provincial Health Care System
Bibliographic record
Abstract
Background: Effective integration of antimicrobial resistant organism (ARO) admission screening into clinical information systems (CIS) can facilitate prompt identification of patients at risk of an ARO and interrupt transmission. However, ARO admission screening remains suboptimal in Alberta, Canada following implementation of the ARO admission screening tool in the provincial CIS. We sought to understand the determinants of adherence with the use of the ARO admission screening tool in the CIS. Methods: A mixed-methods study was conducted using a survey, human factors observations, and qualitative focus groups. Eligible participants included nursing staff and physicians from emergency departments and inpatient units in acute care and acute rehabilitation facilities where the ARO admission screening tool was utilized in the CIS in Alberta, Canada from September 6, 2023 to June 18, 2024 (n=100). A survey (REDCap) explored staff perceptions and experiences using the tool in the CIS. Observations and interviews of nursing staff completing the tool were guided by the Systems Engineering Initiative for Patient Safety model. Virtual (Zoom) semi-structured focus groups explored barriers and enablers of using the tool guided by the Theoretical Domains Framework. Descriptive analysis of survey responses was conducted using Microsoft Excel (Version 2409). Field notes and focus group transcripts were used for a rapid qualitative, thematic analysis. A weaving narrative by theme was used to integrate survey results with findings from the observations and focus groups. Results: There were 527 survey respondents representing all 5 health zones, 5 nurses observed and 20 interviews conducted by the human factors team, and 24 participants in 6 focus groups. Focus group participants represented different sized hospitals (12-1,099 beds) with varying ARO admission adherence rates (29-83%). Three emergent themes arose: context, the ARO admission screening tool, and the individual. Contextual factors included time constraints, increasing nursing workload, competing priorities, lack of patient cooperation, and a need to increase interactions with infection prevention and control programs. Attributes of the tool impacting completion included location of the tool within the CIS, lack of prompts, and multiple sources of information required to complete the tool. At an individual level, themes arose related to experience, perceptions of ARO screening, and lack of training that influenced completion of the tool. Conclusions: Among the emergent themes, multiple determinants were identified influencing the use of the ARO admission screening tool in the provincial CIS. These findings will help inform future strategies to improve ARO admission screening and reduce ARO transmission.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".