Strategies to Improve Antimicrobial Resistant Organism Admission Screening in a Provincial Healthcare System: Consensus-Based Approach
Bibliographic record
Abstract
Background: Adherence with antimicrobial resistant organism (ARO) admission screening is suboptimal, despite clinical support tools in clinical information systems (CIS) to facilitate the process. Behaviour change techniques to improve adherence are needed. However, in a resource-constrained healthcare system, strategies that motivate healthcare workers (HCWs) to align their practices with infection prevention and control (IPC) policies need to be prioritized. Methods: An online survey (REDCap) and a virtual (Zoom) consensus meeting using a modified nominal group technique with online voting was conducted among HCWs, IPC, and the CIS staff in September and October 2024, respectively, to achieve consensus on a prioritized list of interventions to improve ARO admission screening at acute care and acute rehabilitation facilities (n=100) in Alberta, Canada. Interventions from the Behaviour Change Wheel were mapped to barriers/enablers influencing screening adherence. Each intervention was judged across the APEASE criteria (Acceptability, Practicality, Effectiveness, Affordability, Side Effects, Equity) using a 5-point Likert Scale. Consensus to include interventions required >4 criteria with >80% agreement, consensus to exclude required >4 criteria with 80%. Interventions that did not reach consensus were discussed to determine whether to include in the final candidate list. Attendees were asked to vote on their top three interventions from the final candidate list. Results: There were 15 barriers and one enabler to ARO admission screening, mapped to 43 unique interventions. Of these, 16 interventions addressed more than one barrier/enabler, while 27 interventions only addressed a single barrier. Fifty-nine respondents completed the survey. Most respondents (63%) were IPC staff, 20% were nurses, and 17% were other HCWs (including IPC physicians). Nine interventions met criteria to include in the candidate list, 26 were excluded, and 8 interventions did not reach consensus in the survey and were discussed. There were 32 attendees at the consensus meeting (53% IPC staff and physicians, 34% clinical staff, 13% other provincial teams). Three interventions were selected: 1) creating a nursing task to complete the tool in the CIS when an admission order is signed, 2) add a banner on the CIS Storyboard when the tool is not complete, and 3) develop a best practice guideline for frontline staff on ARO admission screening. Conclusions: The survey and consensus meeting were efficient methods to determine a prioritized list of interventions, which will be implemented and evaluated, to improve ARO admission screening in Alberta.
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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 | 0.074 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".