Toward intersectional and culturally relevant sex and gender analysis in health research
Bibliographic record
Abstract
Current institutional frameworks in sex- and gender-based analysis (SGBA) are promising, but significant gaps remain in their relation to recent developments in research praxis. In this paper we draw from our own experiences with a national health research funding agency, the Canadian Institutes of Health Research (CIHR), to critically examine the uptake and implementation of its current frameworks and practices of sex and gender analysis in health research. We conducted semi-structured interviews with a cohort of 18 health researchers alongside an institutional policy analysis to show how sex and gender have been understood, integrated, and addressed within the agency and initiative. Our findings reveal that attention to date has focused on representation (human and data) while deeper justice issues that are attentive to intersectionality, positionality and reflexivity-remain ambiguous. Finally, we discuss possible strategies for institutions to improve the uptake of knowledge, training, and policy to better support intersectional and culturally-relevant frameworks across the diverse research community.
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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 | 0.474 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.031 | 0.169 |
| Scholarly communication | 0.043 | 0.029 |
| Open science | 0.006 | 0.046 |
| Research integrity | 0.005 | 0.014 |
| 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".