Sex and gender considerations in implementation interventions to promote shared decision making: A secondary analysis of a Cochrane systematic review
Bibliographic record
Abstract
BACKGROUND: Shared decision making (SDM) in healthcare is an approach in which health professionals support patients in making decisions based on best evidence and their values and preferences. Considering sex and gender in SDM research is necessary to produce precisely-targeted interventions, improve evidence quality and redress health inequities. A first step is correct use of terms. We therefore assessed sex and gender terminology in SDM intervention studies. MATERIALS AND METHODS: We performed a secondary analysis of a Cochrane review of SDM interventions. We extracted study characteristics and their use of sex, gender or related terms (mention; number of categories). We assessed correct use of sex and gender terms using three criteria: "non-binary use", "use of appropriate categories" and "non-interchangeable use of sex and gender". We computed the proportion of studies that met all, any or no criteria, and explored associations between criteria met and study characteristics. RESULTS: Of 87 included studies, 58 (66.7%) mentioned sex and/or gender. The most mentioned related terms were "female" (60.9%) and "male" (59.8%). Of the 58 studies, authors used sex and gender as binary variables respectively in 36 (62%) and in 34 (58.6%) studies. No study met the criterion "non-binary use". Authors used appropriate categories to describe sex and gender respectively in 28 (48.3%) and in 8 (13.8%) studies. Of the 83 (95.4%) studies in which sex and/or gender, and/or related terms were mentioned, authors used sex and gender non-interchangeably in 16 (19.3%). No study met all three criteria. Criteria met did not vary according to study characteristics (p>.05). CONCLUSIONS: In SDM implementation studies, sex and gender terms and concepts are in a state of confusion. Our results suggest the urgency of adopting a standardized use of sex and gender terms and concepts before these considerations can be properly integrated into implementation research.
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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 | 0.083 | 0.241 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".