Design and pilot evaluation of a brief intervention to reduce transphobia and improve attitudes of government officials to address legal gender affirmation needs of transgender people in Peru
Bibliographic record
Abstract
Legal gender affirmation – legal name and gender marker change – is an important health-promoting health determinant for transgender people. In Peru, the state's failure to universally recognize transgender people’s legal identity limits standardized legal affirmation procedures, including the paucity of government officials trained in gender affirmation strategies. This project, in partnership with Registro Nacional de Identificación y Estado Civil (RENIEC) and transgender communities, designed and piloted a group-based intervention to sensitize government officers to the importance of gender-concordant identity documents. Between August 2017 and February 2018, three in-person group intervention sessions were held (each 3–4 hours) with 51 government officers. Guided by Gender Affirmation and Structural Violence Frameworks, the intervention utilized Adult Learning Theory and applied storytelling and testimonials as pedagogy. Pre-/post-test surveys were administered (19 true/false items, summed to create an index score measuring knowledge and attitudes toward transgender people). Within-person changes in pre-/post-intervention scores were evaluated using paired t-tests. Pre-/post-test data were available for 41 participants. After the intervention, there were improvements in knowledge and more favorable attitudes toward transgender people (pre-test mean = 14.09, SD = 2.33 vs. post-test mean = 15.62, SD = 1.82; difference = 1.53, 95% CL = 0.60, 2.67; t-test = 3.30 [df = 46]; p = 0.002). The intervention was feasible to conduct and garnered high acceptability. The results suggest the promise of this brief intervention for future research and testing before potential later implementation and scale-up to increase the capacity of government officers to address legal gender affirmation for transgender people in Peru.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".