Systematic review of instruments for measuring sex and gender attributes: Assessment of measurement properties and utility in research on clinical and functional outcomes
Bibliographic record
Abstract
Abstract Objectives To identify and synthesize instruments used to assess sex and gender attributes in relation to clinical and functional outcomes in adults. Methods We searched four databases from inception to November 20, 2023, to identify English-language studies on clinical and functional outcomes that used instruments of sex and gender attributes. On July 20, 2025, we searched MEDLINE for studies on the measurement properties of the instruments previously identified. We used Holmbeck and colleagues’ evidence-based criteria and rated quality based on measurement properties including frequency of use by independent researchers, validity and reliability in relevant populations, and sufficiency of information for replication and appraisal. Results Of the 12,964 unique records identified through our primary searches, 46 studies met our inclusion criteria. Eighteen of the studies had male-only samples, five had female-only samples, and the remaining included samples featuring both sexes. One study included transgender and nonbinary people. These studies utilized a total of 34 instruments, of which eight assessed sex and 26 assessed gender attributes. We assigned high quality scores to three instruments measuring sex and three instruments measuring gender attributes. We rated the remaining instruments on sex attributes and 14 instruments on gender attributes as moderate in quality, and the rest as low quality. Conclusion The measurement properties of instruments measuring attributes of sex and gender in relevant populations varied. Construct validity was the most reported property, and test-retest reliability was the least reported on. Validation of the scores in non-binary samples is limited. Further validation and application of high-quality instruments is timely for advancing equitable practices and policies.
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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 | 0.052 | 0.230 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".