Exploring the properties of the Intersectional Discrimination Index — Major in Canadian Veterans with chronic pain
Bibliographic record
Abstract
Introduction: Social determinants of pain have been difficult to study because few measurement scales exist. The Intersectional Discrimination Index - Major (InDI-M) is a new, 13-item scale with potential for use in pain research, though little is known about its utility across different populations. This study evaluates the measurement properties of the InDI-M among Canadian military Veterans with chronic pain. Methods: Data were collected through an online survey that included the InDI-M and questions related to pain, depression, anxiety, and intersectional identities. Results: < 0.05). The Interpersonal Violence subscale showed stronger properties than the Systemic Inequity subscale. The sample was predominantly white and male, reducing response distribution. Discussion: The InDI-M appears to be more useful for capturing experiences of interpersonal violence among military Veterans and less so for inequity and marginalization, and it may hold value for pain research. It may capture intersectional discrimination or negative experiences during military service among this population. Suggestions for future research directions are provided.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".