Sex and gender matter in health research: addressing health inequities in health research reporting
Bibliographic record
Abstract
Attention to the concepts of 'sex' and 'gender' is increasingly being recognized as contributing to better science through an augmented understanding of how these factors impact on health inequities and related health outcomes. However, the ongoing lack of conceptual clarity in how sex and gender constructs are used in both the design and reporting of health research studies remains problematic. Conceptual clarity among members of the health research community is central to ensuring the appropriate use of these concepts in a manner that can advance our understanding of the sex- and gender-based health implications of our research findings. During the past twenty-five years much progress has been made in reducing both sex and gender disparities in clinical research and, to a significant albeit lesser extent, in basic science research. Why, then, does there remain a lack of uptake of sex- and gender-specific reporting of health research findings in many health research journals? This question, we argue, has significant health equity implications across all pillars of health research, from biomedical and clinical research, through to health systems and population health.
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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 | 0.462 | 0.617 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".