Pharmacist Prescribing in Pediatric and Neonatal Acute Care: An Observational Study
Bibliographic record
Abstract
OBJECTIVE: The intent of this project was to objectively describe the frequency of pharmacist prescribing in acute care pediatrics and neonatology and to determine the medications most often prescribed by pharmacists practicing in a jurisdiction that permits pharmacists' prescribing. METHODS: This was a subgroup analysis of a retrospective observational study using prescribing data from an electronic medical record system used in 5 acute care hospitals (1 pediatric, 4 primarily adult but with pediatric and neonatal units) within Calgary, Alberta, Canada. RESULTS: Considering orders for pediatric or neonatal patients only, there was a mean (SD) of 126 (226) prescriptions per pharmacist per year, with a wide range (1-1101 per year). Considering only the 9 clinical pharmacist full-time equivalents (FTEs) assigned to pediatrics and/or neonatology (i.e., not including dispensary pharmacist FTE), this represents 572 prescriptions per clinical pharmacist FTE per year (726 in pediatrics and 380 in neonatology). The most common medication classes on pediatric units included anti-infective agents, central nervous system agents, and gastrointestinal agents. In NICUs, blood formation, coagulation and thrombosis agents (mainly iron), electrolytes, caloric and water balance agents (primarily sodium supplements), and vitamins were also commonly prescribed by pharmacists. CONCLUSIONS: As the scope of pharmacy practice expands to include prescribing, health team leadership can use these data to support incorporation of this role into practice. Prescribing pharmacists can ensure appropriate use of many medications used in acutely ill infants and children, potentially improving efficiency and quality of care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".