“We’re all in an abusive relationship with the health-care system”: Collective memories of transgender health care
Bibliographic record
Abstract
Collective memory work allows participants to recall, examine, and analyze their memories and experiences within a broader cultural context to see how their individual experiences link to collective, shared experiences of similar and/or different groups. This study utilized collective memory work to engage six trans participants in an examination of their individual experiences with health care. During a four-hour focus group, participants engaged in this process of discourse analysis and came to collective agreements about the meaning of their stories, the intentions of the author, and the intentions of others in their shared lived experience. In this paper, we will provide a thorough and rich description of the participants’ memories and their collective analysis, which highlights the interconnection between perceptions of oneself and their experiences with the health-care system. Our analysis revealed participants felt they had a toxic relationship with the health-care system. In particular, they discussed how health-care professionals left trans people tremulously asking for services, uncertain if they would receive care, what the quality of the care would be, and whether they would be treated respectfully. When discussing positive health-care experiences, participants highlighted when fears and anxieties were not realized, but all instances reflected some inappropriate actions. The results from this study will contribute to research on trans health care by providing a nuanced understanding of how health-care experiences impact trans communities collectively, as well as the ways in which health practices can be improved.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".