The Relation between Perceived Need for Mental Health Treatment, DSM Diagnosis, and Quality of Life: A Canadian Population-Based Survey
Bibliographic record
Abstract
OBJECTIVES: Prevalence estimates of mental disorders were designed to provide an indirect estimate of the need for mental health services in the community. However, recent studies have demonstrated that meeting criteria for a DSM-based disorder does not necessarily equate with need for treatment. The current investigation examined the relation between self-perceived need for mental health treatment and DSM diagnosis, with respect to quality of life (QoL) and suicidal ideation. METHODS: Data came from an Ontario population-based sample of 8116 residents (aged 15 to 64 years). The University of Michigan Composite International Diagnostic Interview was used to diagnose mood, anxiety, substance use, and bulimia disorder according to DSM-III-R criteria. We categorized past-year help seeking for emotional symptoms and (or) perceiving a need for treatment without seeking care as self-perceived need for treatment. We used a range of variables to measure QoL: self-perception of mental health status, a validated instrument that measured well-being, and restriction of activities (current, past 30 days, and long-term). RESULTS: Independent of subjects' meeting criteria for a DSM-III-R diagnosis, self-perceived need for treatment was significantly associated with poor QoL (on all measures) and past-year suicidal ideation. CONCLUSIONS: Self-perceived need for mental health treatment, in addition to DSM diagnosis, may provide valuable information for estimating the number of people in the population who need mental health services. The relation between self-perceived need for treatment and objective measures of treatment need requires future study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".