The Intersectional Discrimination Index: Development and validation of measures of self-reported enacted and anticipated discrimination for intercategorical analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Although intersectional approaches have gained traction in population health research, quantitative discrimination and health studies have tended to focus on a single axis of discrimination (e.g., racism, homophobia). As few discrimination measures function across multiple social identities or positions, we developed the Intersectional Discrimination Index (InDI) for intercategorical intersectionality research, including measures of Anticipated (InDI-A), Day-to-Day (InDI-D), and Major (InDI-M) discrimination that do not require attribution to particular grounds. METHODS: We conducted a validity and reliability study with 2016 online survey panel data from Canada and the United States (n = 2583). Internal consistency and dimensionality of the InDI-A were evaluated with exploratory and confirmatory factor analyses. Construct validation included known-groups comparisons, associations with psychological distress, and convergence with existing discrimination measures. Test-retest reliability was examined in a subgroup (n = 150). RESULTS: We found support for use of the InDI-A as a unidimensional scale. As hypothesized, racial and sexual/gender minorities reported higher frequencies of all discrimination types (all p < 0.001), and discrimination varied across intersectional categories. Each InDI component was significantly positively associated with psychological distress after controlling for potential confounders. Frequency scores were strongly positively correlated with existing scales. Intraclass correlation coefficients for test-retest reliability of anticipated, lifetime day-to-day, and lifetime major discrimination ranged from 0.70 to 0.72. CONCLUSIONS: Final InDI measures include the 9-item InDI-A, 9-item InDI-D, and 13-item InDI-M, for which we have found initial evidence of construct validity and reliability. In combination with sociodemographic information, the InDI measures can be used to evaluate the role of discrimination as a mediator of intersectional health inequalities, and to monitor the prevalence and impacts of discrimination in heterogeneous populations.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".