Exploring the promises of intersectionality for advancing women's health research
Bibliographic record
Abstract
Women's health research strives to make change. It seeks to produce knowledge that promotes action on the variety of factors that affect women's lives and their health. As part of this general movement, important strides have been made to raise awareness of the health effects of sex and gender. The resultant base of knowledge has been used to inform health research, policy, and practice. Increasingly, however, the need to pay better attention to the inequities among women that are caused by racism, colonialism, ethnocentrism, heterosexism, and able-bodism, is confronting feminist health researchers and activists. Researchers are seeking new conceptual frameworks that can transform the design of research to produce knowledge that captures how systems of discrimination or subordination overlap and "articulate" with one another. An emerging paradigm for women's health research is intersectionality. Intersectionality places an explicit focus on differences among groups and seeks to illuminate various interacting social factors that affect human lives, including social locations, health status, and quality of life. This paper will draw on recently emerging intersectionality research in the Canadian women's health context in order to explore the promises and practical challenges of the processes involved in applying an intersectionality paradigm. We begin with a brief overview of why the need for an intersectionality approach has emerged within the context of women's health research and introduce current thinking about how intersectionality can inform and transform health research more broadly. We then highlight novel Canadian research that is grappling with the challenges in addressing issues of difference and diversity. In the analysis of these examples, we focus on a largely uninvestigated aspect of intersectionality research - the challenges involved in the process of initiating and developing such projects and, in particular, the meaning and significance of social locations for researchers and participants who utilize an intersectionality approach. The examples highlighted in the paper represent important shifts in the health field, demonstrating the potential of intersectionality for examining the social context of women's lives, as well as developing methods which elucidate power, create new knowledge, and have the potential to inform appropriate action to bring about positive social change.
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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 | 0.140 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.055 | 0.137 |
| Scholarly communication | 0.043 | 0.042 |
| Open science | 0.007 | 0.054 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 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".