Feasibility and acceptability of collecting biobehavioral data from Latinx transgender and nonbinary people
Bibliographic record
Abstract
Background: Latinx transgender and nonbinary (TGNB) people are at high risk of poor health compared to cisgender (i.e., non-TGNB) people. Most studies about the health of Latinx TGNB people have collected self-reported data. There is an urgent need for inclusion of biobehavioral measures to better understand and target the underlying mechanisms of health disparities among Latinx TGNB people. We conducted the first study to assess the feasibility and acceptability of collecting biobehavioral data that we are aware of in this population. Methods: Participants were recruited from an existing longitudinal study of TGNB individuals and through outreach to the community. Data collection involved structured surveys, saliva samples, actigraphy, sleep dairies, and blood pressure monitoring. We administered a survey with Likert scale items and open-ended questions to assess the acceptability of our study procedures. Results: The sample consisted of 41 Latinx TGNB adults with a mean age of 35.7 (+/- 11.9) years. The majority of participants completed all phases of data collection, demonstrating feasibility of study procedures. Acceptability was overall good, but there were a few exceptions; some participants disliked ambulatory blood pressure monitoring or saliva collection. The open-ended responses on the acceptability survey organized around each aspect of the data collection procedures, and the following additional categories were derived: 1) perceived benefits of participating in research, 2) interest in receiving individual data, and 3) the importance of TGNB cultural awareness for study staff. Conclusions: This study is one of the first to demonstrate the feasibility and acceptability of collecting biobehavioral data with Latinx TGNB people. Biobehavioral research can help to illuminate mechanisms underlying the substantial health disparities experienced by this multiply minoritized group. Future research with this population should consider augmenting self-reported data by including biobehavioral measures relevant for the research question.
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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 | 0.061 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".