Eliminating LGBTIQQ Health Disparities: <i>The Associated Roles of Electronic Health Records and Institutional Culture</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For all humans, sexual orientation and gender identity are essential elements of identity, informing how we plan and live our lives. The historic invisibility of sexual minorities in medicine has meant that these important aspects of their identities as patients have been ignored, with the result that these patients have been denied respect, culturally competent services, and proper treatment. Likely due to historic rejection and mistreatment, there is evidence of reluctance on the part of LGBT patients to disclose their sexual orientation (SO) or gender identity (GI) to their health care providers. There is some perception of risk in sharing SO and GI for many patients who have had bad prior experiences. Despite these risks, we argue that we can improve the quality of care provided this population only by encouraging them to self-identify and then using that information to improve quality of care. One strategy both to prompt patient self-identification and to store and use SO and GI data to improve care centers on the use of electronic health records. However, gathering SO and GI data in the EHR requires a workforce that knows both how to obtain and how to use that information. To develop these competencies, educational programs for health professionals must prepare students and educators to elicit and to use sexual orientation and gender identity information to improve care while simultaneously ensuring the safety of patients, trainees, and staff and faculty members as SO and GI become openly discussed and integral parts of ongoing medical discussion and care. As determination of SO and GI demographics becomes more common in health research, we will more fully understand the health risks for all the LGBTIQQ populations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it