Social Maltreatment and Symptomatology: Validating the Social Discrimination and Maltreatment Scale—Short Form in a Diverse Online Sample
Bibliographic record
Abstract
There are few psychometrically valid measures of exposure to social maltreatment that simultaneously assess sexism, racism, and anti-LGBTQ+ (lesbian, gay, bisexual, transgender, queer, and other nonheteronormative) behavior, despite the commonness of these phenomena. The Social Discrimination and Maltreatment Scale (SDMS) meets this requirement but is, as a result, somewhat lengthy (36 items). This article introduces a short form of the SDMS containing only half the number of items but generally retaining the psychometric qualities of the original measure. The 18-item Social Discrimination and Maltreatment Scale—Short Form (SDMS-SF) consists of six SDMS stem items (e.g., I have been disrespected, People made cruel or demeaning jokes about me ) each of which is rated according to how often it had happened “because of my sex,” “because of my race,” and “because of my sexual orientation or gender identity.” In the SDMS online sample ( N = 528), SDMS-SF Sexism, Racism , and Cisheterosexism subscales were validated by confirmatory factor analysis and were internally consistent (α = .91–.95) and highly correlated with the original SDMS subscales ( r = .94 in all cases). All SDMS-SF subscales correlated with self-reported anxiety, depression, and posttraumatic stress (mean r = .29), corresponding to a medium effect size. In all but one instance, related SDMS and SDMS-SF subscales did not differ significantly in the strength of their association with symptomatology. Together, these results suggest that the SDMS-SF is a reliable and valid measure of social discrimination, generally equivalent to the SDMS despite containing only half as many items.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".