Gaps and barriers in the implementation and functioning of antimicrobial stewardship programmes: results from an educational and behavioural mixed methods needs assessment in France, the United States, Mexico and India
Bibliographic record
Abstract
Abstract Background Evidence shows limited adherence to antimicrobial stewardship (AMS) principles. Objectives To identify educational gaps and systemic barriers obstructing adherence to AMS principles. Methods A mixed-methods study combining a thematic analysis of qualitative interviews (January–February 2021) and inferential analysis of quantitative surveys (May–June 2021) was conducted. Participants from France, the USA, Mexico and India were purposively sampled from online panels of healthcare professionals to include infectious disease physicians, infection control specialists, clinical microbiologists, pharmacologists or pharmacists expected to apply AMS principles in their practice setting (e.g. clinic, academic-affiliated or community-based hospital). A gap analysis framework guided this study. Results The final sample included 383 participants (n = 33 interviews; n = 350 surveys). Mixed-methods findings indicated suboptimal knowledge and skills amongst participants to facilitate personal and collective application of AMS principles. Survey data indicated a gap in ideal versus current knowledge of AMS protocols, especially amongst pharmacologists (Δ0.95/4.00, P < 0.001). Gaps in ideal versus current skill levels were also measured and were highest amongst infectious control specialists (Δ1.15/4.00, P < 0.001), for convincing hospital executives to allocate resources to AMS programmes. Already existing systemic barriers (e.g. insufficient dedicated time/funding/training) were perceived as being aggravated during the COVID-19 pandemic (72% of survey participants agreed). Reported gaps were highest in India and France. Conclusions The educational needs of professionals and countries included in this study can inform future continuous professional development activities in AMS. Additional funding should be considered to address perceived systemic barriers. Local assessments are warranted to validate results and suitability of interventions.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".