Identifying Opportunities for Antimicrobial Stewardship in a Tertiary Intensive Care Unit: A Qualitative Study
Bibliographic record
Abstract
Background: Antimicrobial stewardship (AMS) encompasses numerous interventions that seek to improve antimicrobial usage, as inappropriate use of antimicrobials may result in the promotion of antimicrobial resistance, patient harm, and increased costs. AMS is of particular interest in intensive care units (ICUs) where antimicrobial use is extensive. Few qualitative studies have sought to identify the perceived attitudes and beliefs of intensive care clinicians around AMS. Objectives: To understand ICU nursing and physician priorities and preferences around AMS and possible AMS interventions for implementation in the ICU. Methods: Using consecutive sampling, semi-structured one-to-one interviews were conducted with ICU nursing and physician staff at a tertiary hospital in BC, Canada. Results: Nine participants (seven nurses and two physicians) were interviewed, and themes were identified and categorized as: opportunities to improve AMS in the ICU, barriers to AMS in the ICU, and possible future AMS interventions for implementation in the ICU. Opportunities identified included: clinician activities (improved communication, de-escalation, ICU nurse assessment) and support (infectious disease and antibiotic experts, AMS presence). Barriers identified included: knowledge gaps (infectious disease and antibiotic knowledge, AMS awareness), AMS and ICU integration (nursing role in AMS, AMS efficacy in ICU), and environment (competing priorities, critical care context). Interventions identified included: organisational (EMR modifications, checklists, algorithms), learning (infectious disease and antimicrobial education, audit, and feedback), and nursing intervention (antibiotic review, prompting reassessment).
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".