Unveiling the Mosaic of Identity: Illuminating Intersectionality through a Delphi Process for Quantitative Questionnaire Development
Bibliographic record
Abstract
Objectives: To develop and validate a landmark survey tool that will quantify intersectionality in large-scale, population-based sample surveys, using intersectionality-based experiences of physical activity participation as its developmental terrain. Methods: Drawing on qualitative scholarship on intersectionality and existing quantitative surveys that assess social positions, we created a draft questionnaire comprising five modules: 1) sex/gender identity/gender expression, 2) sexual orientation/sexuality, 3) race/ethnicity/(im)migration/religion/language/Indigeneity, 4) (dis)ability/health/physical appearance, and 5) socioeconomic status. A draft of the questionnaire was then reviewed by experts via the Delphi approach, with agreement among 75% of participants and response stability across multiple rounds as determinants of consensus. Results: Qualitative studies (n=199) informed the development of outward-facing items, capturing how individuals are perceived by others. Pre-existing survey items (n=77) informed the creation of self-identifying items, reflecting how individuals self-identify. A draft of the questionnaire, consisting of 27 items, was then evaluated by experts following the three rounds of Delphi surveys, which gauged the accessibility of the questionnaire (e.g., language used, length) and content using 5-point Likert scale. Experts also provided feedback via responses to open-ended questions that guided the revision of the questionnaire. These responses were ranked, analyzed, and synthesized to obtain ≥75% consensus. Conclusion: The developed questionnaire represents a significant step forward in advancing understanding of the complex, intersectional nature of social participation and marginalization. Its application across various topics in physical activity and health research promises to enrich and inform future investigations in the field.
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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 | 0.289 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".