Perceived Need for Mental Health Treatment in a Nationally Representative Canadian Sample
Bibliographic record
Abstract
OBJECTIVE: The optimal method of determining how many people in the general population need help for emotional problems remains unclear. This study aimed to examine the prevalence and correlates of self-perceived need for mental health services (that is, help seeking and perceived need) in a large, population-based sample. METHODS: Data came from the Canadian Community Health Survey 1.2 (n = 36,816, respondent age 15 years and over, and response rate 77%). Respondents were asked whether they had sought help in the past year from any professional for emotional problems and whether they felt they needed help for emotional symptoms but had not sought treatment. The Composite International Diagnostic Interview (CIDI) was used to make DSM-IV mental disorder diagnoses. RESULTS: The past-year prevalences of help seeking and perceived need were 8.7% and 2.9%, respectively. After adjusting for the presence of DSM-IV disorders assessed in the survey, sociodemographic factors, illness severity, social supports, and the presence of physical health conditions were associated with help seeking and perceived need. Independent of DSM diagnoses, sociodemographics, and social supports, perceived need and help seeking were associated with increased levels of distress, disability, and suicidal ideation and attempts. CONCLUSIONS: This study illustrates that, in addition to the presence of a DSM diagnosis, the respondent's self-perceived need for mental health treatment is important in the assessment of need for mental health services in the community.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| 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".