Mental Health Treatment Dropout and Its Correlates in a General Population Sample
Bibliographic record
Abstract
BACKGROUND: Dropping out of mental health treatment prematurely may affect treatment outcome. However, we have limited knowledge about the epidemiology of mental health treatment dropout. The objectives of this analysis were to estimate the rates of dropout in individuals who had received mental health treatment provided by different health professionals and to identify factors associated with mental health treatment dropout. METHODS: Data from the Canadian Community Health Survey-Mental Health-Well-being were used. Participants who had used mental health services in the past 12 months were included in the analysis (n=3556). The percentages dropping out of mental health treatment provided by various health professionals were estimated. Logistic regression was used to identify factors associated with treatment dropout. RESULTS: The overall rate of dropout from mental health treatment in the past 12 months was 22.3%. Participants who had used services provided by family doctors/general practitioners had the lowest rate of dropout (11.8%). The dropout rate was 22.7% in those who were treated by psychiatrists and was 21.9% in participants who had seen psychologists. Young (15-25 years), nonwhite and individuals who reported having had a mood disorder or having had substance dependence were more likely to terminate treatment prematurely. CONCLUSIONS: In Canada, a large percentage of individuals who use mental health services prematurely terminate their treatment. Clinical factors may play important roles in treatment dropout. Patients with substance dependence and those with mood disorders have a high risk of treatment dropout.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".