Dissident Genders: Intersectionality and Assistance in Healthcare Networks
Bibliographic record
Abstract
Introduction: The persistence of a cisgender binary structure continues to marginalize individuals whose identities fall outside normative gender categories. Understanding the perspectives of gender-dissident individuals is necessary for creating health environments that uphold respect and dignity in care for people of all genders. Objectives: To analyze the perspectives of dissident genders on care and to understand their perception of the professional approach in healthcare networks. Method: This cross-sectional qualitative study employed a subject-partner methodological approach, grounded in the theoretical frameworks of Akotirene, Butler and Preciado. Data were collected through 2 virtual focus groups with gender-dissident participants and through field diaries involving the subject-partner. The data were analyzed using Minayo’s content analysis methodology. Results: Three thematic dimensions of meaning emerged: 1) Being-itself, capturing experiences of identity, recognition, and the challenges of existing in a cisnormative world; 2) Care structuring from-in the Unified Health System, highlighting how professional beliefs and institutional norms restrict access, humanization, and continuity of care; and 3) Strategies for advancement, emphasizing inclusive practices, representativeness, and the need for structural and educational change. Discussion and Conclusion: Experiencing care within healthcare networks remains marked by inconsistent recognition of gender identity. Inclusive care requires subverting cisnormative values, strengthening professional education, and promoting active participation of gender-dissident people. The subject-partner framework underscores their role as co-constructors of knowledge and 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".