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—authors’ response
Bibliographic record
Abstract
Dear Editor, We thank you for the opportunity to respond to the commentary made by Martha Carolina Valderrama-Rios, Laura Cristina Nocua-Báez, Carlos Arturo Álvarez-Moreno and Jorge Alberto Cortes from the Universitario Nacional, Bogotá, Colombia. We appreciate their contributions to this field, in the hopes of better understanding the factors that influence antibiotic prescription among healthcare professionals. Valderrrama-Rios and colleagues highlighted that the ‘fear of patient deterioration and complications’ was the most common reason for antibiotic prescription. We would encourage the authors to publish their full methods and results to better understand the context of their study. For instance, how were the questions designed, and how was the analysis conducted to determine that the ‘fear of patient deterioration or complications’ was the most common reason for antibiotic prescription that is not concordant with guideline recommendations? What other factors were provided as options for the survey respondents? Have the authors investigated as part of their surveys why fear was the most common reason, and what the root causes of this fear are? In the exploratory phase of this study, the concept of ‘fear’ potentially driving antimicrobial prescription was brought forward by only one participant during interviews in the context of the COVID-19 pandemic, especially the first wave: A clinical pharmacist mentioned, ‘we may have changed our first-line strategy for some infections, in that we had to give a lot of macrolides in the beginning to comply with the guidelines […] we prescribed a bit more macrolides, because we feared… But then, not at all during the second wave’. The nature and cause of the fear is unclear in this one interview. As ‘fear’ was not a main theme derived from our interviews, we did not investigate it further in the survey. Following our publication, other studies have reported both fear and deficit in knowledge as factors associated with the unwarranted use of antibiotics, especially azithromycin, during the early stages of the COVID-19 pandemic.1 From a psychological perspective, fear and knowledge (among other factors) can form a complex relationship in mediating human behaviour.2 In the context of healthcare professionals’ use of antimicrobial agents, fear could be a by-product of underlying beliefs worth further investigation (e.g. perceived consequences of using broad versus narrow spectrum antibiotics for a patient affected by a bacteria susceptible to drug resistance, or of delaying prescription to perform appropriate testing). Our study was designed to focus on the implementation of antimicrobial stewardship (AMS) programmes and principles in practice. For example, our survey investigated approaches to ensuring AMS principles were adhered to by practising professionals, and it was found that 90% of surveyed participants selected ‘Gain insight into inappropriate behaviour (i.e. understand rationale for behaviour)’ as an important step in addressing prescribers’ non-compliance to AMS principles. Hence, whichever the driver to non-compliance, this approach can be a first step to identifying a suitable intervention aimed at enhancing adherence to AMS principles and relevant guidelines. We acknowledge the importance for other studies to investigate in depth the drivers to non-compliance to further inform this recommended approach. In conclusion, we would like to thank the authors and editor for the opportunity to elaborate further on this topic. We believe that both initiatives are important to inform future studies, in addition to the design of effective interventions aimed at optimizing prescription of antimicrobial agents individually and as a collective. This correspondence refers to a research study that was supported with education research funds from bioMérieux SA to AXDEV Group Inc. The correspondence was written independently from bioMérieux SA. Co-authors P.L., M.A. and S.P. are employees of AXDEV Group Inc. D.A.G. has received grant support from Merck Sharp & Dohme (MSD) and was a consultant over 2 years ago to MeMEd, as well as an adviser for Becton, Dickinson and Company and Spero Therapeutics. M.V.V. has received educational grants and consultant fees from MSD, Pfizer, WEST and bioMérieux. A.A. received consultant fees from Abbott and bioMérieux.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".