Mental Health Literacy Reduces the Impact of Internalized Stigma on Older Adults' Attitudes and Intentions to Seek Mental Health Services
Bibliographic record
Abstract
OBJECTIVES: Older adults are the least likely age group to seek mental health services, and internalized stigma is an important reason why. We sought to further our understanding of which older adults are particularly likely to be affected by internalized stigma, and why, by investigating mental health literacy (MHL) as a moderator within the internalized stigma model of help-seeking. METHODS: We utilized a conditional process analysis of cross-sectional, secondary data from 350 distressed older adults. Participants completed an online survey consisting of measures of distress, perceived control, experiential avoidance, MHL, public and self-stigma of seeking help, help-seeking attitudes, and conditional help-seeking intentions. RESULTS: MHL moderated the internalized stigma model; distressed older adults with lower MHL were more likely to have public stigma internalized as self-stigma, which then reduced their intentions to seek help. More specifically, low MHL magnified the negative effect of self-stigma on attitudes and intentions. CONCLUSIONS: These results increase our understanding of which older adults are less likely to seek mental health services: distressed older adults with poor MHL and high self-stigma. CLINICAL IMPLICATIONS: MHL is a malleable construct that can be targeted by interventions designed to increase help-seeking among distressed older adults in need of professional help.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".