A mixed-methods needs assessment for an antimicrobial stewardship curriculum in pediatrics
Bibliographic record
Abstract
Abstract Objective: Antimicrobial stewardship (AS) education initiatives for multidisciplinary teams are most successful when addressing psychosocial factors driving antimicrobial prescribing (AP) and when they address the needs of the team to allow for a tailored approach to their education. Design: We conducted a mixed-methods embedded study as a needs assessment, involving quantitative analysis of AS concerns observed by pharmacists through an audit while attending clinical team rounds, as well as qualitative semi-structured interviews based on the Theoretical Domain Framework (TDF) to identify psychosocial barriers and facilitators for antimicrobial prescribing for an inpatient general pediatric service. We analyzed the data using deductive and inductive methods by mapping the TDF to a model for social determinants of antimicrobial prescribing (SDAP) in pediatric inpatient health care teams. Setting: The Clinical Teaching Unit (CTU) and Pediatric Intensive Care Unit (PICU), at a tertiary care pediatric hospital in Canada. Participants: Interviews (n = 23) with staff and resident physicians, nurse practitioners, and pharmacists. Results: Psychosocial facilitators and barriers for AS practice in the PICU and CTU which were identified included: collaboration, shared decision-making, locally accessible guidelines, and an overarching goal of doing right by the patient and feeling empowered as a prescriber. Some of the barriers identified included the norm of noninterference, professional comparisons, limited resources, feeling inadequately trained in AS, emotional prescribing, and a pejorative monitoring system. Conclusions: Our findings identified barriers and facilitators to AS decisions on pediatric inpatient teams as well as actionable needs in psychosocial-based AS education.
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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 | 0.039 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 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".