Integrating Intersectionality Theory for Informing Health Policymaking
Bibliographic record
Abstract
ABSTRACT Equitable and evidence‐informed healthcare policies are critical to ensure that marginalized populations receive the health and social care they deserve. Intersectionality theory is an analytical tool for examining how individual and social categorizations affect populations and their outcomes. It enables assessing the negative impact of systems of oppression which systematically marginalize certain populations by limiting their access to essential resources and opportunities within society. Therefore, intersectionality theory is a valuable lens for understanding and addressing health disparities and inequities. The purpose of this paper is to explore how incorporating intersectionality theory into health policymaking can help develop equitable and effective policies that are more responsive to the needs of marginalized populations. The key features of intersectionality theory and their application in informing health policy are discussed. We outline three broad ways how intersectionality theory can assist in health policymaking. These include: disaggregated assessment to inform health policy work, intersectional policy impact evaluation, and fostering meaningful engagement of marginalized populations in policymaking. It is argued that integrating intersectional theory can enable health policymakers to examine the interplay of social identities and structural factors that drive disparities. Health policymakers can examine how and why the intersecting identities and structures can create disparities. Health policymakers can meaningfully engage marginalized populations in the planning and implementation of policies and do targeted advocacy to develop specific policy directions for the delivery of equitable services and care.
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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 | 0.054 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| 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".