Incorporating intersectionality theory into population health research methodology: Challenges and the potential to advance health equity
Bibliographic record
Abstract
Intersectionality theory, developed to address the non-additivity of effects of sex/gender and race/ethnicity but extendable to other domains, allows for the potential to study health and disease at different intersections of identity, social position, processes of oppression or privilege, and policies or institutional practices. Intersectionality has the potential to enrich population health research through improved validity and greater attention to both heterogeneity of effects and causal processes producing health inequalities. Moreover, intersectional population health research may serve to both test and generate new theories. Nevertheless, its implementation within health research to date has been primarily through qualitative research. In this paper, challenges to incorporation of intersectionality into population health research are identified or expanded upon. These include: 1) confusion of quantitative terms used metaphorically in theoretical work with similar-sounding statistical methods; 2) the question of whether all intersectional positions are of equal value, or even of sufficient value for study; 3) distinguishing between intersecting identities, social positions, processes, and policies or other structural factors; 4) reflecting embodiment in how processes of oppression and privilege are measured and analysed; 5) understanding and utilizing appropriate scale for interactions in regression models; 6) structuring interaction or risk modification to best convey effects, and; 7) avoiding assumptions of equidistance or single level in the design of analyses. Addressing these challenges throughout the processes of conceptualizing and planning research and in conducting analyses has the potential to improve researchers' ability to more specifically document inequalities at varying intersectional positions, and to study the potential individual- and group-level causes that may drive these observed inequalities. A greater and more thoughtful incorporation of intersectionality can promote the creation of evidence that is directly useful in population-level interventions such as policy changes, or that is specific enough to be applicable within the social contexts of affected communities.
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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 | 0.414 | 0.378 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.008 | 0.064 |
| Scholarly communication | 0.027 | 0.041 |
| Open science | 0.011 | 0.035 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".