Mental health services use among adults with or without mental disorders: Do development stages matter?
Bibliographic record
Abstract
BACKGROUND: Mental health services (MHS) use is a complex behaviour that does not only concern individuals with current mental disorder. To date, few studies have examined age-related contextualisation of MHS use. Reasons for seeking help may vary according to development stages in adulthood. AIMS: This study aimed to determine which predisposing, enabling and need factors, using Andersen's model, were associated with MHS use according to adult development stages among individuals with or without current psychiatric diagnosis. METHODS: Three age groups were examined: 18- to 29-year-olds (n = 775), 30- to 49-year-olds (n = 1,560) and 50- to 64-year-olds (n = 960). Data were obtained from the Montreal Longitudinal Catchment Area Study. Bivariate and multivariate logistic regression analyses were conducted for each age group separately to determine which predisposing, enabling and need factors were associated with MHS use in the past 12 months. RESULTS: For 18- to 29-year-olds, one enabling factor (Internet search) and two need factors (presence of major depressive disorder and number of stressful events) were positively associated with MHS use. For 30- to 49-year-olds, one predisposing factor (family history of mental disorder), four enabling factors (not currently working or in school, perceiving neighbourhood disorder, social cohesion and Internet searching) and one need factor (major depressive disorder) correlated with help seeking. For 50- to 64-year-olds, two predisposing factors (family history of mental disorder and higher self-perceived stigma), two enabling factors (low satisfaction in personal relationship and Internet searching) and one need factor (alcohol dependence) were associated with MHS use. CONCLUSIONS: Factors associated with MHS use differ according to adult development stages. Programmes and policies should be based on age-related contextualisation to increase MHS use.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".