Chilean Trans Men: Healthcare Needs and Experiences at the Public Health System
Bibliographic record
Abstract
Introduction: Research shows high rates of discrimination against the transgender population in healthcare centers, which has negative consequences for their quality of life. There are few studies in this area that focus upon the experiences of trans men, although, they tend to experience higher levels of violence and stigmatization in healthcare settings. Method: We explored the perceptions of 14 Chilean trans men regarding discrimination in healthcare centers from an ethnographic approach, with the aim of analyzing their experiences in these facilities, and identifying their needs in relation to healthcare. Results: The majority of trans men perceived health centers as a source of discrimination. The most important expressions of discrimination involve questioning their gender identity, and disregarding their social names and pronouns. In view of this, interviewees placed fear of discrimination as the main factor preventing them from being attended at healthcare centers. On the other hand, having access to health workers who are trained in providing care to the transgender population, being provided with information, and the possibility of receiving psychosocial support are the main factors promoting their attendance at these facilities. Discussion: The study has implications both for health professionals and public policies focused on the transmasculine community. Recommendations are made for professionals and administrative staff to provide respectful and sympathetic care in order to generate spaces free of discrimination that encourage attendance at healthcare centers, improving the welfare of trans men.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".