Reducing public stigma toward bipolar disorder: II. Mechanisms of stigma reduction
Bibliographic record
Abstract
BACKGROUND: Investigating how mental health anti-stigma interventions work is crucial to increasing intervention efficacy. This paper represents the second part of a larger feasibility study comparing the effects of a novel intervention (loving-kindness meditation; LKM) and a standard education-contact strategy (EC), relative to a control condition, on reducing stigma toward bipolar disorder (BD). This study examined potential mechanisms of the effects of LKM and EC on the reduction of BD stigma found in Paper 1. METHOD: A total of 376 undergraduate students were randomly assigned to EC, LKM, or a control condition. The indirect effects of EC and LKM relative to control on stigma, via positivity and negativity toward others and knowledge of BD, were tested. RESULTS: The effect of EC on lowering stigma toward a hypothetical person with BD was suggested to occur via more knowledge about BD. More positivity toward others possibly explained the effect of LKM and EC on lowering stigma toward a hypothetical person with BD and increasing willingness for future contact with a confederate with BD. Negativity toward others possibly explained the effect of EC on lowering stigma toward a hypothetical person with BD. LIMITATIONS: The cross-sectional design was a key limitation of analyses testing mechanisms of intervention. CONCLUSIONS: Findings highlight that education about BD may be important in reducing stigma related to the disorder. Additionally, enhancing positivity toward others may be a potential mechanism through which stigma reduction occurs across different intervention approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".