The burden of an identity: Coping strategies for sexual and gender minority individuals in pharmacy practice
Bibliographic record
Abstract
PURPOSE: Sexual or gender minority (SGM) individuals are known to experience stigma and discrimination in pharmacy settings. It is also known that SGM individuals may delay or avoid care in pharmacies due to the stress associated with these experiences. Aside from avoidance, however, little is known about how SGM individuals cope with stigma and discrimination and how their coping strategies may influence their healthcare behaviors. Therefore, this study aimed to characterize how SGM individuals cope with stigma and discrimination associated with their SGM identity in pharmacy settings. METHODS: This was a qualitative study conducted using semistructured interviews. A total of 31 SGM individuals were recruited across 2 Canadian provinces. Interviews followed a topic guide designed according to the minority stress model to elicit participants' lived experiences in community pharmacies. Thematic analysis was used to identify and interpret themes. RESULTS: Five themes were identified that described coping strategies used by SGM individuals when encountering minority stress processes in pharmacy settings: avoidance (avoiding or withdrawing from care), seeking support (from people or settings), perseverance (when faced with no other option), concealment (of SGM identity), and lowering expectations (of pharmacist knowledge and competence). CONCLUSION: Our findings support the notion that individuals cope in different ways and across a wide spectrum of behaviors. Those who avoid care, conceal their SGM identity, or are forced to persevere through interactions may be at increased risk for both physical and mental health disparities. Those who seek support or lower expectations may also be at risk for reduced access to quality care.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".