Characterizing pharmacist prescribers in Alberta using cluster analysis
Bibliographic record
Abstract
Abstract Objectives Legislative and regulatory bodies in Canada have authorized pharmacists to prescribe in different provinces. Albertan pharmacists have the broadest prescribing scope. Our objective was to cluster Albertan pharmacists into different prescriber groups based on their self-reported prescribing practice and to compare the groups according to practice settings, the proportion of Additional Prescribing Authority (APA) pharmacists and support experiences. Methods A cross-sectional survey was administered among a sample of 700 Albertan practicing registered pharmacists in 2013 to identify their involvement in different types of prescribing activities. Cluster analysis was used to group participants based on their reported prescribing practices. Chi-squared test was used to compare prescriber groups by practice settings and the proportion of APA pharmacists. One-way analysis of variance was used to compare the groups by their support experiences. Key findings Three major groups of pharmacist prescriber were identified – ‘renewal prescriber’ (74%), ‘Modifier’ (17%) and ‘Wide ranged prescriber’ (9%). Prevalence of ‘renewal prescriber’ in the community setting was 85.8% whereas ‘Modifier’ was predominant (66.7%) in the collaborative setting. Higher support experience facilitated the wide range prescribing. Pharmacists with APA were most likely to be classified into ‘Modifier’ (17.6%) or ‘Wide ranged prescriber’ (13.8%) groups than the ‘renewal prescriber’ group (3.1%). Conclusions Although legislation allowed Albertan pharmacists to have the broadest scope of prescribing authority, few are practicing with the fullest scope. Prescribing practice varies based on practice setting and support experience. Future research could explore factors influencing the types of adoption and measure the shifting of prescribing type over time.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".