Social maltreatment as trauma: Posttraumatic correlates of a new measure of exposure to sexism, racism, and cisheterosexism.
Bibliographic record
Abstract
OBJECTIVE: , fifth edition, text revision (DSM-5-TR) trauma. Yet there is a relative lack of research systematically examining these events, their intersectionality, and links to posttraumatic stress (PTS). The purpose of this study was to develop a comprehensive measure of social discrimination and maltreatment (SDM) and to examine whether these events can serve as potential traumatic stressors, above-and-beyond classic trauma exposure. METHOD: A 36-item Social Discrimination and Maltreatment Scale (SDMS), consisting of three subscales (sexism, racism, and cisheterosexism) and a total score, was developed and validated in a sample of 528 adults. RESULTS: s = 265 and 263). Marginalized groups each endorsed the most relevant SDMS subscale (e.g., people of color reporting more racism and women reporting more sexism). The total SDM score was associated with PTS even when controlling for general trauma exposure, and there was a linear relationship between the number of elevated SDMS subscales and PTS scores. CONCLUSIONS: exposure to sexism, racism, and cisheterosexism may be significant sources of PTS. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".