Applying the Concept of Epistemic Injustice as a Philosophical Window to Examine Discrimination Experiences of LGBTQIA+ Migrants With Nurses
Bibliographic record
Abstract
Both stigma and discrimination, defined as a lack of knowledge of and a sense of discomfort in providing care to lesbian, gay, bisexual, transgender, queer, intersex, and + (LGBTQIA+) migrants, was found to manifest in a sample of LGBTQIA+ migrants who received nursing care in a recent study. The study concluded that nurses continue to have a limited understanding of the experiences of LGBTQIA+ migrants in the Canadian context, and that LGBTQIA+ migrants continue to have troubling 'care' experiences with nurses. Miranda Fricker has developed the concept of epistemic injustice drawing on feminist philosophy and social epistemology. Epistemic injustice refers to unfair treatment of a person by judging them as 'not a knower' in a communicative situation. For example, in a few circumstances when LGBTQIA+ migrants were admitted to psychiatric units due to suicide ideations as a direct result of identifying as a LGBTQIA+ migrants, the medical and nursing team responded with 'They are in Canada now. It is safe here!' and 'So, you are [LGBTQIA + ]! What's the big deal?' These unjust statements reflect an epistemic situation in which the hearer is negating what was heard, that is, that the speaker's intersecting identities of LGBTQIA+ and new immigrant has directly led to suicide ideation. The concept of epistemic injustice helps to frame this situation as one where the care provider is not doing justice to the needs of LGBTQIA+ migrants. This article draws on the narrative of an LGBTQIA+ migrant who is not recognised as a credible source of knowledge about their own lives and needs in the context of Canadian nursing care. Epistemic injustice helps to understand how stigma and discrimination is produced in this community by the very nursing profession who ostensibly want to help them.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.035 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| 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".