PP16 International Approaches To Integrating Sex And Gender In Health Technology Assessment
Bibliographic record
Abstract
Introduction Sex differences and gender inequalities in health are increasingly recognized in health technology assessment (HTA), but their analysis remains inconsistent. Despite advancements in addressing ethical, organizational, and social issues in HTA, neglecting sex/gender (S/G) considerations can distort its outcomes. This initiative aimed to enhance equity by exploring international experiences and identifying frameworks for S/G-sensitive HTA processes. Methods The first phase involved an exploratory analysis to assess the inclusion of S/G in methodological and report prioritization processes, using both Medical Subject Heading terms and free keywords. The search was then expanded to include international experiences, frameworks, and tools incorporating a S/G approach from organizations such as the World Health Organization, International Network of Agencies for Health Technology Assessment, European Network for Health Technology Assessment, Cochrane Collaboration, and other frameworks (GRADE, VALIDATE). HTA agencies and ministries of health from Latin America and high-income countries were reviewed. Data were extracted, focusing on the practical application of S/G in HTA. Results No specific tools were identified, and S/G perspectives were generally absent, with “gender” mainly referring to the male/female distinction. The Cochrane Collaboration has a S/G Methods Group and a S/G in Systematic Reviews Planning Tool. Both the Cochrane Collaboration and GRADE incorporate S/G in equity frameworks (PROGRESS-Plus). Ecuador and Argentina (Instituto de Efectividad Clínica y Sanitaria) integrate gender in the Sustainable Development Goals. The National Institute for Health and Care Excellence includes S/G in qualitative study appraisals, and Canada’s Drug Agency (CDA) advocates for gender equity in health care. The Institute for Quality and Efficiency in Health Care (IQWiG) in Germany emphasizes the need to account for gender differences in health assessments as part of general HTA methods, and the French National Authority for Health (HAS) proposes integrating gender into public health policies and health technology guidance. Conclusions S/G perspectives are internationally recognized as a key axis of inequity. Several entities incorporate these perspectives into documents and frameworks (the Cochrane Collaboration, CDA’s real-world evidence guidelines, IQWiG’s general methods). HAS plans methodological frameworks, and both the Institute for Clinical Effectiveness and Health Policy and IQWiG promote non-sexist/neutral language guidelines. The next exploratory phase would be to see whether and how they are being used in HTA processes.
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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 | 0.152 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.017 | 0.029 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.032 | 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".