Psychometric evaluation of the Intersectional Discrimination Index for use in Brazil
Bibliographic record
Abstract
This cross-sectional study evaluated the configural and metric structures of the Intersectional Discrimination Index (InDI), an instrument that measures anticipated (InDI-A), dat-to-day (InDI-D), and major (InDI-M) discrimination. Data from a broader study, focused on the impacts of discrimination on the mental health of women living in Brazil, were used. Approximately 1,000 women, selected according to a convenience sampling scheme, answered the InDI and questions about sociodemographic characteristics in an electronic form that was administered in 2021. Exploratory factor analyses and exploratory structural equation modeling were applied to the first half of the sample; for the second, confirmatory factor analysis was conducted. Taken together, the findings suggest that each of the three measures is one-dimensional. However, unlike the study that originally proposed the InDI for use in Canada and the United States, we observed the presence of residual correlations in the three subscales evaluated, all of which were suggestive of content redundancy between specific pairs of items. The three measures showed moderate to strong factor loadings and acceptable fit to the data. InDI exhibited reasonable internal validity, potentially becoming a valuable instrument for investigating the health effects of intersectional discrimination in Brazil. Future studies should evaluate the consistency of these findings, examine the scalar structure of the instrument, and analyze its invariance among different marginalized groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".