Ranking of Curricular Content by Pharmacy Students and Community Pharmacists
Bibliographic record
Abstract
A cross-sectional survey was conducted to determine the relative rankings of 17 key components in an undergraduate program. The degree of discrepancy between curricular content and that of student and pharmacist expectations was also of interest. An online questionnaire was emailed to both groups in one Canadian province. Respondents considered four questions related to the nature and adequacy of education they did receive (pharmacists) or should receive (students) and the relative importance of key topic areas (along 11-point scales). The response rate was 31.0 (students) and 10.8 percent (pharmacists). As expected, both students and pharmacists identified therapeutics and patient counselling as critical focal points for the program, while the importance of compounding was mixed. Most topics were deemed as best handled during the didactic program, with students seeing greater value in learning a larger portion of two skills (injection training and managerial duties) post-graduation. In conclusion, discrepancies were indeed found. For students, topics such as injection training and minor ailment prescribing were perceived as receiving too little attention, while communication, pharmaceutical sciences, and professionalism received too much attention. In a significant departure in perspective, pharmacists rated communication, ethical decision-making, and professionalism almost two points higher than did students.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".