Perceived Need, Mental Health Literacy, Neuroticism and Self- Stigma Predict Mental Health Service Use Among Older Adults
Bibliographic record
Abstract
OBJECTIVES: Older adults are the least likely age group to seek mental health services. However, few studies have explored a comprehensive range of sociodemographic, psychological, and social barriers and facilitators to seeking treatment in later life. METHODS: A cross-sectional, national sample of Canadian older adults (55+, N = 2,745) completed an online survey including reliable and valid measures of predisposing, enabling, and need characteristics, based on Andersen's behavioral model of health, as well as self-reported use of mental health services. Univariate and hierarchical logistic regressions predicted past 5-year mental health service use. RESULTS: Mental health service use was most strongly and consistently associated with greater perceived need (OR = 11.48) and mental health literacy (OR = 2.16). Less self-stigma of seeking help (OR = .65) and greater neuroticism (OR = 1.57) also predicted help-seeking in our final model, although their effects were not as strong or consistent across gender, marital status, and age subgroups. CONCLUSIONS: The need category was crucial to seeking help, but predisposing psychological factors were also significant barriers to treatment. CLINICAL IMPLICATIONS: Interventions that target older adults high in neuroticism by improving perceptions of need for treatment, mental health literacy, and self-stigma of seeking help may be particularly effective ways of improving access to mental health services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".