Transgender and Gender Diverse People’s Fear of Seeking and Receiving Care in Later Life: A Multiple Method Analysis
Bibliographic record
Abstract
The purpose of the current research is to understand concerns about receiving care in a sample of transgender, gender nonbinary, and gender diverse (TGD) adults across the lifespan. A total of 829 participants, predominantly from the United States and Canada, aged 18–70, completed the Trans Metlife Survey on Later-Life Preparedness and Perceptions in Transgender-Identified Individuals (TMLS) section on caregiving and are included in this study. We found middle-aged adults, people of Color, and people living with a disability reported the highest level of concern for their ability to function independently because of financial resources, physical concerns, cognitive impairment, or a lack of someone to care for them. Researchers found five overarching thematic categories: (a) No concerns, (b) Anticipated discrimination, (c) Loss of control, (d) Quality of life, and (e) General concerns. Practice implications include recommendations for practitioners to develop care plans with TGD residents and clients to learn the best strategies for affirming their gender identity (e.g., clothing preferences) and to assist TGD residents and clients with the completion of advance directives to allow them to outline their end-of-life care plan, including instructions for gender affirmative care in the event of incapacitation (e.g., dementia, stroke).
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".