Gender Harmony: A Case for Nursing Informatics
Bibliographic record
Abstract
The design of digital health information systems around a conflated gender/sex binary contributes to health inequities. Lack of specific information that supports affirming communication lead to inappropriate care, disrespectful encounters with healthcare staff, and avoidance of health services by clients who have been harmed by misgendering, deadnaming and being outed. The HL7 International Gender Harmony Model (HL7 GHM) supports the design, implementation and use of DHIS that enable affirming clinical interactions and care. This case study will demonstrate how applying the HL7 GHM can address the harms reported in a recently published account of one patient in Canada.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.037 | 0.091 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".