“We're still alive, much to everyone's surprise”: The experience of trans older adults living with dementia in an ageist, cisgenderist, and cogniticist society
Bibliographic record
Abstract
Trans and non-binary older adults living with dementia experience forms of marginalization, pathologization, and discrimination embedded in epistemic violence that leads them to be mistreated and dismissed as knowledgeable subjects. Based on empirical findings from a Canadian study examining the experiences of trans and non-binary people living with dementia and their carers, we combat this epistemic violence by focusing on the first-hand narratives of this population and their carers. Narrative interviews were conducted with six participants (N = 6): four carers of trans and non-binary adults living with dementia and two trans (binary) people living with dementia. Through a thematic analysis, we examine the unique aspects of living with dementia as a trans or non-binary person. First, the findings show how cogniticism impacts the experience of gender identity and cisgenderism, for example through blocked surgeries, excessive gatekeeping, and not being taken seriously by practitioners. Second, the findings discuss how dementia impacts gender identity and cisgenderism, for example, by increasing the need for formal care that can in turn increase vulnerability to structural violence. Third, the findings illustrate how cisgenderism and gender identity impact the experience of dementia and cogniticism, for example by limiting care options and the ability to advocate for oneself. Fourth, the findings highlight the silo mentality among practitioners, since most of them do not work with an intersectional lens. The article concludes by offering recommendations.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| 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".