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Record W3013293959 · doi:10.5688/ajpe7864

A Historical Discourse Analysis of Pharmacist Identity in Pharmacy Education

2020· article· en· W3013293959 on OpenAlexaff
Jamie Kellar, Elise Paradis, Cees van der Vleuten, Mirjam G.A. oude Egbrink, Zubin Austin

Bibliographic record

VenueAmerican Journal of Pharmaceutical Education · 2020
Typearticle
Languageen
FieldNursing
TopicHealthcare Education and Workforce Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPharmacistPharmacyIdentity (music)Discourse analysisMedical educationMedicineLinguisticsFamily medicineArtAestheticsPhilosophy

Abstract

fetched live from OpenAlex

Objective. To determine the discourses on professional identity in pharmacy education over the last century in North America and which one(s) currently dominate. Methods. A Foucauldian critical discourse analysis using archival resources from the American Journal of Pharmaceutical Education ( AJPE ) and commissioned education reports was used to expose the identity discourses in pharmacy education. Results. This study identified five prominent identity discourses in the pharmacy education literature: apothecary, dispenser, merchandiser, expert advisor, and health care provider. Each discourse constructs the pharmacist's professional identity in different ways and makes possible certain language, subjects, and objects. The health care provider discourse currently dominates the literature. However, an unexpected finding of this study was that the discourses identified did not shift clearly over time, but rather piled up, resulting in students being exposed to incompatible identities. Conclusion. This study illustrates that pharmacist identity constructs are not simple, self-evident, or progressive. In exposing students to incompatible identity discourses, pharmacy education may be unintentionally impacting the formation of a strong, unified healthcare provider identity, which may impact widespread practice change.

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)0.011
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.005
Science and technology studies0.0110.014
Scholarly communication0.0080.009
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.080
GPT teacher head0.506
Teacher spread0.426 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations77
Published2020
Admission routes1
Has abstractyes

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