The Challenges of Professional Development in the Evolving World of Pharmacy Education
Bibliographic record
Abstract
The primary purpose of schools and colleges of pharmacy is to produce pharmacists capable of providing competentpatient centered care. To accomplish this goal, pharmacy students must learn and retain a great deal of knowledge aswell as develop professional attitudes and behaviors. In recent years, several articles have been published questioningthe professionalism of pharmacy students and whether colleges of pharmacy are promoting professionalism(Hammer, 2003; Chisholm, 2004). Professionalism has a broad definition and encompasses every aspect of the dutyof a pharmacist. The definition of professionalism and a discussion of professional socialization roadblocks areimportant considerations when establishing guidance to students and faculty. Responsibilities of the student andresponsibilities of the educator should be clear to all involved. E-professionalism presents a new consideration whenoutlining professionalism standards to pharmacy students and should be addressed as well. Colleges of pharmacymust develop methodologies to aid in the development of professionalism among pharmacy students in all types oflearning environments, whether a student attends a traditional campus or a distance campus. Finally, with theimplementation of distance campuses, professionalism is presented with new and unique challenges which requirecontinuous evaluation to prevent negative outcomes.
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 | 0.031 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".