Advancing Research on LGBTQ Microaggressions: A Psychometric Scoping Review of Measures
Bibliographic record
Abstract
Understanding the nature and consequences of LGBTQ microaggressions is critical to fostering equity and wellbeing among sexual and gender minorities. Yet little guidance is available for researchers seeking psychometrically robust measures of subtle LGBTQ slights, invalidations, and insults. To address this gap, we conducted a scoping review of multi-item quantitative measures that included at least one question addressing LGBTQ microaggressions. This article reports the study characteristics and psychometric properties of 27 original measures we identified and their subsequent adaptations. The article concludes with an assessment of strengths and limitations of LGBTQ microaggression measurement, highlighting aspects of measurement innovation on which future researchers can build. As microaggressions remain a powerful and underexplored mechanism of sexual and gender minority oppression, this review will help to both advance methodological quality in this critical research area and enhance our understanding of how microaggressions manifest in the lives of LGBTQ individuals.
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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 | 0.129 | 0.349 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.027 | 0.034 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".