Mental Illness Stigma and Microaggressions: An Experimental Study of Familiarity and Relationship Quality
Bibliographic record
Abstract
Abstract Purpose: Familiarity (i.e., relationship closeness) and relationship quality (i.e., the degree of negativity/positivity) have been suggested as critical components affecting stigmatizing attitudes towards those with mental illnesses. The current study experimentally tested components of, and expanded upon, a recent theoretical framework by Corrigan and Nieweglowski (2019), which proposes a convex (u-shaped) curvilinear relationship between familiarity and stigma (i.e., people hold the most stigma towards others at the lowest andhighest levels of familiarity) rather than a linear one (i.e., stigma simply decreases as familiarity increases). By examining how both familiarity and relationship quality affect public stigma broadly, as well as microaggressions specifically, this research adds to the growing body of literature on mental illness stigma. Methods: Undergraduate students (N = 242) were randomly assigned to read one of six vignettes via a 2(quality: positive vs. negative) x 3(familiarity: co-worker, cousin, romantic partner) between-subjects factorial design. Following, participants completed measures assessing stigmatizing attitudes and microaggression endorsement towards the vignette character. Results: There was a significant main effect of relationship quality on stigmatizing attitudes only; on average, those in the three negative conditions had significantly higher stigmatizing attitudes than those in the positive conditions. Conversely, familiarity only had a significant main effect on microaggressions, wherein higher familiarity groups demonstrated higher microaggression endorsement. No significant interactions were found for either variable. Conclusion: These results suggest that stigmatizing attitudes and microaggressions may be functionally different, and further research is required to clarify current theoretical frameworks in understanding how relational contexts impact these negative attitudes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".