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Record W2884925755 · doi:10.3390/pharmacy6030071

Pharmacists Becoming Physicians: For Better or Worse?

2018· article· en· W2884925755 on OpenAlexaff
Eugene Y. H. Yeung

Bibliographic record

VenuePharmacy · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsPsychologyMedical educationMedicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.029
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.010
Scholarly communication0.0120.019
Open science0.0010.009
Research integrity0.0090.013
Insufficient payload (model declined to judge)0.0140.003

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.

Opus teacher head0.095
GPT teacher head0.463
Teacher spread0.368 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations8
Published2018
Admission routes1
Has abstractyes

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