Mental Health Care Use in Later Life: Results from a National Survey of Canadians
Bibliographic record
Abstract
OBJECTIVE: To estimate the proportion of older adults who have used mental health services in the past 12 months among those who meet the criteria for one or more Diagnostic and Statistical Manual of Mental Disorders (DSM), Fourth Edition, 12-month psychiatric disorders. We also examine the factors associated with mental health care use in this population. METHOD: We used secondary data from the Canadian Community Health Survey: Mental Health and Well-Being (CCHS 1.2). We first estimated the proportion of adults aged 55 years and older who used a range of mental health services. Next, using logistic regression, we examined the relative contribution of predisposing, enabling, and need characteristics in predicting any service use in this population. RESULTS: Among the 12 792 adults aged 55 years and older in the CCHS 1.2, 513 (4.23%, 95% CI 3.89% to 4.95%) met the criteria for at least one 12-month DSM-IV disorder. Among these respondents, 37% (95% CI 31% to 43%) saw at least one type of mental health care provider in the past 12 months. Visits to a general health care provider for mental health reasons were most common, followed by specialist care. Only psychological distress was significantly and positively associated with using mental health care services. CONCLUSIONS: Over 60% of the older adults who met the criteria for a DSM-IV disorder were not using mental health care services. Social and demographic factors did not predict service use in this population.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".