The Impact of the Big Five Personality Traits on Help-Seeking Stigmas, Attitudes, and Intentions
Bibliographic record
Abstract
Mental illness is common, consequential, and increasing in prevalence. Despite this, mostindividuals do not seek professional help for mental health concerns. Research has begun to investigatethe impact of personality on help-seeking, but it has suffered from many limitations. As such, thepurpose of this study was to establish the effects of the Big Five personality traits on public and selfstigmaof seeking help, help-seeking attitudes, and intentions. We employed hierarchical regressionmodels in a large cross-sectional sample (N = 5712) to evaluate personality traits in the context of otherestablished predictors of help-seeking. Agreeableness had consistent protective effects across all modelsand extraversion was especially protective regarding help-seeking intentions. In contrast to thesebeneficial effects, openness, conscientiousness, and neuroticism had complex relationships with help-seekingconstructs. Our findings have implications for understanding the influence of the Big Five onwhich individuals may be unlikely to seek mental health services in the face of a need. Through thisunderstanding, we can begin to develop targeted strategies directed towards individuals at risk to notseek help for mental health concerns, and increase help-seeking behaviour.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".