SCOPING REVIEW PROTOCOL: STRATEGIES UTILIZED BY PHARMACISTS AND PHARMACY STUDENTS IN PRESCRIBING RELATED DECISION-MAKING
Bibliographic record
Abstract
Pharmacists have long been vital members of the healthcare care team (Gysel & Tsuyuki, 2024), but their role has significantly transformed over the past two decades with the introduction of independent pharmacist prescribing. In Canada, pharmacists can independently prescribe in all 12 provinces and the Yukon. However, this increased responsibility brings with it challenges in the clinical decision-making process. While recent studies have sought to better understand the decision-making process in the context of pharmacy practice, most of the research remains focused on other health practitioners (Edwards et al., 2022; Graham-Clarke et al., 2018). The challenges faced by pharmacists are also not well delineated. It also remains unknown how pharmacists address these challenges within the clinical decision-making process (Mertens et al., 2022; Wright et al., 2019). Gaining a better understanding of how pharmacists address these challenges is imperative in ensuring they are equipped with the skills required to confidently incorporate prescribing into their practice.
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 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 | 0.164 | 0.278 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.105 | 0.023 |
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".