Incorporating intersectionality into quantitative research methods in public health
Bibliographic record
Abstract
Abstract Introduction The use of intersectionality as an explicit theoretical framework in quantitative public health research is relatively recent, and has involved a wide array of study design and statistical methods. As best practices have not been identified, guidance for research design and analysis is needed. Methods We draw on a review of the literature and our own methods publications to present an overview of key considerations in approaching public health research from an intersectional perspective. Results Key considerations differ for descriptive studies of intersectional inequalities and analytic studies of potential causes of those inequalities, as research methodologies and their strengths and limitations differ. For descriptive studies, considerations include specification of intersectional groups, multiplicative vs. additive scale for analysis of effects and interactions, limitations of data sets, whether all intersectional groups are of equal interest, and choosing statistical methods. For analytic studies, considerations include whether potential causal factors are relevant and measurable for all intersections or are specific to some, variable measurement, different options in standardization or control of confounding, and statistical analysis methods. Discussion We present considerations in incorporating intersectionality frameworks, and provide tools for conceptualizing intersectionality-informed quantitative public health research.
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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 | 0.513 | 0.552 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".