Evaluating antibiotic stewardship in a tertiary care hospital in Kerala, India: a qualitative interview study
Bibliographic record
Abstract
OBJECTIVES: To determine what barriers and facilitators to antibiotic stewardship exist within a healthcare facility. SETTING: 1300-bed tertiary care private hospital located in the state of Kerala, India. PARTICIPANTS: 31 semistructured interviews and 4 focus groups with hospital staff ranging from physicians, nurses, pharmacists and a clinical microbiologist. RESULTS: Key facilitators of antibiotic stewardship (AS) at the hospital included a dedicated committee overseeing appropriate inpatient antibiotic use, a prompt microbiology laboratory, a high level of AS understanding among staff, established guidelines for empiric prescribing and an easily accessible antibiogram. We identified the following barriers: limited access to clinical pharmacists, physician immunity to change regarding stewardship policies, infrequent antibiotic de-escalation, high physician workload, an incomplete electronic medical record (EMR), inadequate AS programme (ASP) physical visibility and high antibiotic use in the community. CONCLUSIONS: Opportunities for improvement at this institution include increasing accessibility to clinical pharmacists, implementing strategies to overcome physician immunity to change and establishing a more accessible and complete EMR. Our findings are likely to be of use to institutions developing ASPs in lower resource settings.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".