Routine collection of sexual orientation and gender identity data: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Sexual orientation and gender identity are key social determinants of health, but data on these characteristics are rarely routinely collected. We examined patients' reactions to being asked routinely about their sexual orientation and gender identity, and compared answers to the gender identity question against other data in the medical chart on gender identity. METHODS: We analyzed data on any patient who answered at least 1 question on a routinely administered sociodemographic survey between Dec. 1, 2013, and Mar. 31, 2016. We also conducted semistructured interviews with 27 patients after survey completion. RESULTS: The survey was offered to 15 221 patients and 14 247 (93.6%) responded to at least 1 of the sociodemographic survey questions. Most respondents answered the sexual orientation (90.6%) and gender identity (96.1%) questions. Many patients who had been classified as transgender or gender diverse in their medical chart did not self-identify as transgender, but rather selected female (22.9%) or male (15.4%). In the semistructured interviews, many patients expressed appreciation at the variety of options available, although some did not see their identities reflected in the options and some felt uncomfortable answering the questions. INTERPRETATION: We found a high response rate to questions about sexual orientation and gender identity. Fitting with other research, we suggest using a 2-part question to explore gender identity. Future research should evaluate the acceptability and feasibility of administering these questions in a variety of care settings. These data can help organizations identify health inequities related to sexual orientation and gender identity.
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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 | 0.079 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".