Mental health literacy and help-seeking: the mediating role of self-stigma and emotional intelligence
Bibliographic record
Abstract
When faced with mental health concerns, help-seeking can be a useful means to seek and receive help from formal support sources—such as mental health professionals, as well as informal support sources—such as friends and family. Both the intention and tendency to engage in formal help-seeking are predicted by mental health literacy, self-stigma, and emotional intelligence; however, the role that each of these factors play in relation to informal help-seeking is less clear. The current study examined the predictive value of mental health literacy with respect to both formal and informal help-seeking intentions. Additionally, the current study explored the role of self-stigma and emotional intelligence as possible mediators of these relationships. Undergraduate students ( n = 301) were recruited from a Western Canadian university and completed a series of online questionnaires measuring their formal and informal help-seeking intentions, mental health literacy, self-stigma faced when seeking help, and meta-mood, as an operationalization of emotional intelligence. Results indicated that mental health literacy was a significant positive predictor of formal help-seeking intentions, and that both self-stigma and meta-mood partially mediated this relationship. Furthermore, results showed that mental health literacy did not serve as a significant predictor of informal help-seeking, although mental health literacy did have a significant indirect effect on informal help-seeking, through the mediation of meta-mood. The importance of self-stigma and meta-mood in relation to mental health literacy are highlighted in terms of formal help-seeking outcomes, and the implications of these findings for informal help-seeking are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".