Is employment status associated with baseline symptoms, engagement, and outcomes in naturalistic psychotherapy? Evaluation in a large community mental health agency.
Bibliographic record
Abstract
age = 32.54; 62.9% female; 75.8% White) who attended 115,936 psychotherapy sessions at a Canadian mental health agency between January 2014 and July 2022. Employment status was categorized into nine distinct groups (e.g., full-time, part-time, unemployed and looking for work, unemployed not looking for work, and retirement). Multilevel models examined the association between employment status and baseline symptoms, psychotherapy engagement (e.g., total sessions, early termination), and outcomes (e.g., symptom change). Patients who were unemployed (both looking for and not looking for work) reported higher baseline symptoms and increased odds of suicide concern compared to patients with full-time employment. Contrary to our preregistered hypotheses, patients who were unemployed attended more sessions and showed no significant differences in symptom change or trajectory of change compared to those employed full-time. Retirement was linked to lower baseline symptomology, and both retirement and full-time student status were associated with slower trajectories of change relative to full-time employment. Findings suggest that unemployment is associated with worse baseline mental health but does not hinder psychotherapy engagement and effectiveness. Ensuring accessibility of psychotherapy for unemployed individuals is crucial, given their heightened risk of psychological distress. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".