Examining the Double-Edged Sword Effects of Lay Theories of Mental Health on Perceptions and Treatment of Others with Mental Health Problems
Bibliographic record
Abstract
INTRODUCTION: We examined the implications of viewing mental health problems as changeable through personal effort (incremental lay theory), fixed due to genetics (entity lay theory), or manageable through perseverance on perceptions of others with mental health problems. METHOD: In two preregistered studies, samples of online American participants were randomly assigned to one of four experimental conditions: manageable, incremental (changeable), entity (fixed), or control, and completed self-report measures of onset responsibility, offset efficacy, blame, stigma, willingness to help, and perceived likelihood of success of interventions aimed at helping those with mental health problems. RESULTS: In both studies, the manipulation had a significant effect on each outcome except willingness to help. Compared to the entity (fixed) condition, individuals in the incremental (changeable) condition reported significantly higher offset efficacy and perceived likelihood of success, but also higher onset responsibility and blame. Results for the manageable condition were similar to the incremental (changeable) condition but individuals in the manageable condition also reported lower responsibility (Study 1) as well as lower blame and stigma (Study 2). DISCUSSION: This work informs the ‘double-edged sword’ effects of holding incremental (changeable) or entity (fixed) lay theories concerning mental health problems. Findings also provide evidence that viewing mental health problems as manageable may reduce some of the negative and boost the positive implications associated with incremental (changeable) and entity (fixed) lay theories.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".