Sex and gender analysis in knowledge translation interventions: challenges and solutions
Bibliographic record
Abstract
Sex and gender considerations are understood as essential components of knowledge translation in the design, implementation and reporting of interventions. Integrating sex and gender ensures more relevant evidence for translating into the real world. Canada offers specific funding opportunities for knowledge translation projects that integrate sex and gender. This Commentary reflects on the challenges and solutions for integrating sex and gender encountered in six funded knowledge translation projects. In 2018, six research teams funded by the Canadian Institutes of Health Research's Institute of Gender and Health met in Ottawa to discuss these challenges and solutions. Eighteen participants, including researchers, healthcare professionals, trainees and members of the Institute of Gender and Health, were divided into two groups. Two authors conducted qualitative coding and thematic analysis of the material discussed. Six themes emerged, namely Consensus building, Guidance, Design and outcomes effectiveness, Searches and recruitment, Data access and collection, and Intersection with other determinants of health. Solutions included educating stakeholders on the use of sex and gender concepts, triangulating perspectives of researchers and end-users, and participating in organisations and committees to influence policies and practices. Unresolved challenges included difficulty integrating sex and gender considerations with principles of patient-oriented research, a lack of validated measurement tools for gender, and a paucity of experts in intersectionality. We discuss our findings in the light of observations of similar initiatives elsewhere to inform the further progress of integrating sex and gender into the knowledge translation of health services research findings.
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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 | 0.359 | 0.457 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.024 | 0.048 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".