Who is asking? Requests for antimicrobial prescribing advice received by hospital pharmacists
Bibliographic record
Abstract
Abstract Doctors are perceived as the primary decision makers in antimicrobial therapy, but prescribing decisions are influenced by the multidisciplinary team. Antimicrobial stewardship (AMS) programs formalise interprofessional advice‐giving. No studies capture the advice provided by pharmacists. This study aimed to describe the volume and nature of antimicrobial prescribing advice that healthcare professionals seek from hospital pharmacists. A prospective audit of antimicrobial‐related advice requests received by pharmacists (n = 18) at an Australian public hospital was undertaken in July 2020. Antimicrobial advice was sought from 11 pharmacists on 300 occasions. Most requests (80%) were received by the AMS pharmacist. A mean (range) of 30 (17–40) requests per day was recorded and the AMS pharmacist received 24 (16–31) requests daily. Most requests came from the intensive care unit (22.1%), pharmacy (21.4%), and infectious diseases (17.1%). The AMS pharmacist was mostly contacted by consultants and pharmacists, and other pharmacists were contacted by registrars and junior medical officers. Despite COVID‐19 adaptations, face‐to‐face interaction was most common. This audit demonstrates the value of an AMS pharmacist, and indicates the importance of face‐to‐face interactions and the formalisation of pharmacists' role in prescribing decision‐making. Pharmacists provided antimicrobial advice daily to other healthcare professionals. Further research is required to provide insights into the barriers and enablers to effective advice‐giving interactions.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".