Implementation of additional prescribing authorization among oncology pharmacists in Alberta
Bibliographic record
Abstract
PURPOSE: To describe the practice settings and prescribing practices of oncology pharmacists with additional prescribing authorization. METHODS: A descriptive, cross-sectional survey of all oncology pharmacists in Alberta was conducted using a web-based questionnaire over four weeks between March and April 2016. Pharmacists were identified from the Cancer Services Pharmacy Directory and leadership staff in Alberta Health Services. Descriptive statistics were used to describe the practice setting, prescribing practices, motivators to apply for additional prescribing authorization, and the facilitators and barriers of prescribing. Logistic regression was used to explore factors associated with having additional prescribing authorization. RESULTS: The overall response rate was 41% (71 of 175 pharmacists). Oncology pharmacists with additional prescribing authorization made up 38% of respondents. They primarily worked in urban, tertiary cancer centers, and practiced in ambulatory care. The top 3 clinical activities they participated in were medication reconciliation, medication counseling/education, and ambulatory patient assessment. Respondents thought additional prescribing authorization was most useful for ambulatory patient assessment and follow-up. Antiemetics were prescribed the most often. The median number of prescriptions written in an average week of clinical work was 5. Competence, self-confidence, and the potential impact on patient care/perceived impact on work environment were the strongest facilitators of prescribing. The strongest motivators to apply for additional prescribing authorization were relevancy to practice, the potential for increased efficiency, and advancing the profession. CONCLUSION: The current majority of oncology pharmacist prescribing in Alberta occurs in ambulatory care with a large focus on antiemetic prescribing. Pharmacists found additional prescribing authorization most useful for ambulatory patient assessment and follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".