A cross-sectional study of hospital antimicrobial stewardship programmes in the COVID-19 era
Bibliographic record
Abstract
Background: Antimicrobial resistance (AMR) continues to be a global public health issue amid the COVID-19 pandemic; however, unprecedented demands on hospital antimicrobial stewardship programmes (ASPs) potentially altered their core activities. Objective: We sought to understand how ASPs have been involved in and impacted by the pandemic. Methods: The 2021 Ontario ASP Landscape Survey was developed based on previous provincial questionnaires and emerging literature on the impact of COVID-19 on hospital ASPs. After pre-testing and piloting, the online questionnaire was distributed to hospital antimicrobial stewardship practitioners in the fall of 2021. Descriptive statistics and inductive thematic analysis were performed. Results: The response rate was 78% (98/125 organizations); 96% (94/98) of organizations had or were in the process of formalizing an ASP and 53% (50/94) reported designated funding/resources. Despite 82% reporting no change in dedicated full-time equivalents during the pandemic, ASPs were frequently involved in developing treatment guidelines/clinical pathways (51%), anticipating/managing drug shortages (46%) and obtaining investigational use drugs (32%). While many core ASP activities continued, prospective audit and feedback and prescriber education were modified or suspended by 43% and 40% of programmes, respectively. Decreased frequency, adaptation of activities (i.e. virtual or other technology) and challenges with staffing/resources were commonly reported themes. Knowledge translation (KT) activities and 'collaboration and coordination' also emerged as salient themes. Conclusions: Hospital antimicrobial stewardship practitioners in Ontario have made significant contributions to the pandemic response while continuing to deliver adapted ASP services, despite resource constraints. Moving forward, ASPs will need to continue building capacity while leveraging broader networks to advance the antimicrobial stewardship agenda.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".