Independent prescribing by hospital pharmacists: Patterns and practices in a Canadian province
Bibliographic record
Abstract
PURPOSE: Results of a survey to determine the frequency of prescribing by Canadian hospital pharmacists with independent prescribing authority are reported. METHODS: A Web-based questionnaire was used to collect data on the prescribing activities of a designated group of hospital-affiliated pharmacists in the province of Alberta who had been granted "additional prescribing authorization" (APA) through a peer-review process and were providing clinical pharmacy services in inpatient and/or outpatient settings at the time of the survey (January-March 2014). Descriptive statistics and logistic regression analysis were used to determine the median weekly frequency of prescribing, factors associated with increased use of APA, and perceived prescribing barriers and enablers. RESULTS: The survey response rate was about 50% (77 of 153 eligible pharmacists). The median self-reported number of prescriptions and medication orders written during an average week was 4.2 (interquartile range, 2.0-10.0) per 10 patients. Antibiotics and anticoagulants were the most commonly prescribed medications. Interdisciplinary care team dynamics was rated as a leading enabler of prescribing but also a leading barrier to the exercise of APA. The greatest motivators to apply for APA were the potential for increased efficiency and the potential for enhanced patient care. CONCLUSION: The survey results indicated that, in an average week, hospital pharmacists with APA prescribed for almost half of the patients they cared for as part of the interdisciplinary team. Prescribing most frequently occurred after team discussion and most often involved adjusting dosages based on organ function and clinical assessment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".