Bibliographic record
Abstract
Physicians and pharmacists nowadays are often described as adversaries rather than members of the same team. Some pharmacists apply to medical school later in their careers, and experience obstacles during the transition process. This article details interviews with two physician⁻pharmacists, who each have a past pharmacist license and current physician license. The respondents described the limitations of pharmacists' scope of practice as their main reasons to pursue a medical career. However, the respondents enjoy applying their pharmacy knowledge and experience to improve their medical practice. They do not feel pharmacy seniors and medical recruiters are supportive towards their chase for medical careers. The respondents noted the importance of peer-reviewed articles to promote pharmacist involvement in patient care and collaboration between physicians and pharmacists. Conflicts between physicians and pharmacists tend to happen because of their different focuses on patient care. The respondents do not see themselves having an edge over other medical school applicants, and noted that recruiters could negatively view their pharmacy experience. The respondents believe that physician⁻pharmacists are catalysts to foster collaboration between physicians and pharmacists, because they clearly understand the role of each profession. Nevertheless, the respondents feel that physicians and pharmacists are generally lukewarm towards pharmacists transitioning into physicians.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 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".