Reducing public stigma toward bipolar disorder: I. Interventions and outcomes
Bibliographic record
Abstract
BACKGROUND: Anti-stigma interventions targeting public stigma toward bipolar disorder (BD) may increase treatment seeking, treatment adherence, and quality of life for individuals with this condition. However, such interventions seldom target BD, nor do they use measures of stigma that are ecologically valid, go beyond explicit self-report, or distinguish between different stigma components. METHOD: Undergraduate students (N = 376) were randomly assigned to a novel intervention (loving-kindness meditation; LKM), a common stigma reduction intervention (education-contact; EC), or a control condition. Efficacies of the interventions were tested on explicit stigma toward a hypothetical person with BD (cognitive stigma, affective stigma, behavioral intentions), implicit stigma, and affective stigma and behavioral intentions toward a real-life confederate ostensibly diagnosed with BD. RESULTS: EC reduced some indices of cognitive and affective stigma toward a hypothetical person compared to control; LKM usually did not differ from the other conditions. Both EC and LKM led to more positive behavioral intentions toward the confederate relative to control. Affective stigma toward the confederate and implicit stigma did not differ across conditions. LIMITATIONS: Interventions were brief. Pre-intervention stigma and long-term effects were not assessed. CONCLUSIONS: In this feasibility study, both EC and LKM show some promise for stigma reduction, particularly regarding behavioral intentions toward a confederate. Novel approaches or developments in interventions may be needed to address other stigma components, including implicit stigma.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".