Straight until proven otherwise; improving sexual orientation and gender identity disclosure in healthcare, and its application in Manitoba.
Bibliographic record
Abstract
Background: The 2SLGBTQIA+ community experiences disproportionately poor healthcare outcomes when compared to their cisgender-heterosexual counterparts. This disparity is thought to be primarily due to a lack of disclosure of sexual orientation and gender identity (SOGI) to healthcare providers. Failure of providers to ask patients’ their SOGI is thought to be the greatest barrier to disclosure. Objective: Discuss the best methods to collect SOGI data from a logistical standpoint, and from a patient and provider perspective. Method: Narrative literature review using PubMed and Scopus databases Results: Nine articles were reviewed and their results categorized into four outcome measures: SOGI fields in electronic medical records, data collection method, patient perspectives on SOGI data collection, provider perspectives on SOGI data collection. Conclusions: Adding SOGI to electronic medical records is an important step in improving disclosure. Both patients and providers prefer an indirect method of data collection such as survey format. Patients want to know why their SOGI is being collected. Provider education in delivering queer-competent care is required. Many of these findings could be implemented in the existing healthcare infrastructure in Manitoba.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".