Promoting real-world evidence use for antimicrobial stewardship in Latin America: evaluation of impact of a two-part educational webinar series
Bibliographic record
Abstract
Abstract Background Educational programs on the use of real-world evidence (RWE) in antimicrobial stewardship (AMS) are scarce in Latin America (LATAM). Objectives To develop and evaluate an online educational program supporting LATAM healthcare professionals (HCP)’s ability to use and generate RWE for effective antimicrobial agent use, aligned with AMS principles. Methods Two 90-min webinars were developed by subject matter experts. Changes in knowledge, skills, confidence and attitudes were measured via paired PRE-and POST-intervention survey questions. Satisfaction, intent to change and remaining barriers were surveyed POST-intervention. McNemar and Wilcoxon Signed Rank statistical tests assessed differences in paired dichotomous and ordinal data, respectively. Unpaired data underwent descriptive analysis. Open-ended responses were subject to thematic content analysis (inductive reasoning approach). Results The analysis sample included 741 PRE-intervention survey completers (epidemiologists, infection control specialists, chemists, pharmacists, biologists, microbiologists, bacteriologists and other physicians), with 47 completing the full POST survey (33 following webinar 1, and 14 following webinar 2). A significant increase in the percent of completers who were confident of ‘what constitutes RWE’ was found PRE (31%) to POST (73%) intervention (P < 0.001). Median self-reported skill levels changed from ‘2-basic’ to ‘3-intermediate’ for providing examples of RWE and applying RWE in the context of AMS (P < 0.05). Barriers included low perceived value of RWE by administrators and limited access to appropriate data. Conclusions This education improved HCPs’ confidence in knowing what constitutes RWE. Findings provide direction for future interventions aimed at enhancing access to and appropriate use of RWE to inform AMS in LATAM.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".