The Impact of Moving Beyond Intersection to Integration of LGBTQIA+ Identities on Professional Identity Affirmation
Bibliographic record
Abstract
There have been active calls within pharmacy education literature for the profession to work toward dismantling systemic oppression by elevating the voices of commonly underrepresented and marginalized communities, including the lesbian, gay, bisexual, transgender, queer/questioning, intersex, and asexual(LGBTQIA+) community. There has also been a simultaneously growing interest in understanding how the intersection of one's personal identity with one's professional identity may help to foster greater affirmation within the profession. However, what has not been explored is how intersecting personal and professional identities may enhance the strength of one's LGBTQIA+ identity and therefore result in creating cultures of affirmation in addition to meaningful participation in professional advocacy. We link our lived experiences to a theoretical perspective through the minority stress model to demonstrate how distal and proximal stresses may affect pharmacy professionals' ability to fully integrate their professional and personal identities. Additionally, we call on the academy to strategically address gaps in LGBTQIA+ knowledge, equity, and professional development through research, shifts in culture, and education.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".