Bibliographic record
Abstract
Historically, within digital health information systems, sex and gender have been conflated as a single concept and often have been limited to a binary answer. This has led to inappropriate care, erosion of client trust and avoidance of the healthcare system. Health leaders can improve care for all clients with technical and clinical information practice initiatives. While procurement processes could require digital health systems that utilize modern Gender, Sex, and Sexual Orientation (GSSO) terminology, for most health leaders, technical initiatives will focus on modernizing existing systems to the maximum extent possible. Terminology updates may not be immediately visible to clients, but providing the correct information to clinicians will support respectful client encounters. Simultaneously, clinical information practice initiatives can directly affect clinical encounters. Change management strategies need to include all levels of employees and redesign tools and workflows to support modernized information handling practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".