Dropping Out of Mental Health Treatment: Patterns and Predictors Among Epidemiological Survey Respondents in the United States and Ontario
Bibliographic record
Abstract
OBJECTIVE: The authors interviewed individuals treated for self-described mental health problems in the preceding year to examine patterns and predictors associated with dropping out of treatment. METHOD: Subjects were drawn from respondents to community epidemiological surveys carried out in representative samples of the United States and Ontario populations. Dropouts were those who had left mental health treatment during the prior year for reasons other than symptom improvement. The surveys also assessed potential dropout correlates: sociodemographic characteristics, attitudes about mental health care, disorder type, provider type, and treatment received. RESULTS: The proportion of dropouts did not significantly differ between the United States (19.2%) and Ontario (16.9%), nor did the effects of the predictors differ significantly between the two samples. Sociodemographic characteristics associated with treatment dropout included low income, young age, and, in the United States, lacking insurance coverage for mental health treatment. Patient attitudes associated with dropout included viewing mental health treatment as relatively ineffective and embarrassment about seeing a mental health provider. Respondents who received both medication and talk therapy were less likely to drop out than those who received single-modality treatments. CONCLUSIONS: Mental health treatment dropout is a serious problem, especially among patients who have low income, are young, lack insurance, are offered only single-modality treatments, and have negative attitudes about mental health care. Cost-effective interventions targeting these groups are needed to increase the proportion of patients who complete an adequate course of treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".