The Influence of Intersectionality on Professional Identity Formation among Underrepresented Pharmacy Students
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to explore professional identity formation (PIF) among student pharmacists from underrepresented groups (URGs). METHODS: In this qualitative study, 15 student pharmacists from the University of Georgia and Midwestern University Colleges of Pharmacy were recruited for interviews to explore the influence of intersectionality of race, ethnicity, and gender on PIF. Interview data were analyzed using constructivist grounded theory to identify themes and then further analyzed using Crenshaw's theory of intersectionality, namely structural, political, and representational intersectionality. RESULTS: Intersectionality of identities created situations where participants expressed advantages belonging to certain social categories, while simultaneously being disadvantaged belonging to other social categories. This awareness led to strategies to overcome these collective obstacles for themselves and their communities. Participants then described ways to shift perceptions of how society depicts pharmacists and the pharmacy profession. The results depict these processes and how intersectionality influences PIF for URG student pharmacists. CONCLUSION: The sociocultural aspects of race, ethnicity, and gender influence the PIF of student pharmacists who belong to URGs. Intersectionality helps us better understand the ways in which inequality compounds itself, and this results in URG student pharmacists creating opportunities for belongingness and representation. Resultantly, URGs create opportunities for inclusivity and representation. To continue to facilitate this it is essential for educators and university systems to promote ways to foster and incorporate PIF in student pharmacists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".