Born This Way: Integrating LGBTQIA+ Identities as Pharmacy Practitioners
Bibliographic record
Abstract
Progression through the profession of pharmacy is filled with many milestones that can contribute to feelings of stress, rejection, and isolation. For Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, and Asexual+(LGBTQIA+) students and practitioners, these feelings can be compounded by similar issues experienced by their sexual orientation or gender identity. Historically, LGBTQIA+ students, new practitioners, and seasoned professionals alike have lacked visible role models for how to intersect personal and professional identity in the pharmacy profession. In this paper, the authors describe experiences of intersecting personal queer identities with professional pharmacy identities; exploring barriers to integration and developing solutions to overcome these barriers. The authors also share how the formation of a collective of LGBTQIA+ practitioners and educators has led to a unified voice to advocate for the advancement of LGBTQIA+ healthcare in pharmacy education and practice. This manuscript will provide readers with a guide to navigate and address issues with the integration of personal and professional identity to lead to practice that validates personal identity as important, valuable, and affirmed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".