Using Best-Worst Choice Methodology in a Survey of Pharmacists Regarding Pharmacy Practice Skills Teaching
Bibliographic record
Abstract
Objective. To conduct a survey of practicing pharmacists in which best-worst choice methodology was used to prioritize pharmacy practice skills for inclusion in a pharmacy curriculum in New Zealand. Methods. A literature search and review of pharmacy curricula were conducted, and the findings were used to develop a best-worst choice survey instrument regarding inclusion of pharmacy practice skills in the pharmacy curriculum. The survey was sent to registered pharmacists and intern pharmacists in New Zealand. Participants were asked to prioritize 16 skills in terms of their importance and relevance to pharmacy practice. Results. Of the 3836 pharmacists invited to participate in the survey, 388 completed the questionnaire. Comprehensive chronic disease management, specialty medications, and medicines use review were the top three prioritized skills. Injections, independent prescribing, and specialty compounding were the skills ranked as having the lowest priority. The pharmacists’ gender, age, practice setting, and ethnicity all influenced their skill prioritization. The pharmacists emphasized skills required in their current practice but deemphasized some skills that were emerging professional responsibilities. Conclusion. If curricular reform is to include new skills that are largely unfamiliar to or deemed unimportant by practicing pharmacists, quality assurance of students’ experiential education will be needed. Furthermore, preceptor education about changing expectations for pharmacy graduates’ skill sets must be adequately developed and implemented to ensure that preceptors provide students with opportunities to practice the full range of skills they will need in practice and provide them with accurate assessment and helpful feedback.
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 | 0.032 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".