Work-related treatment for major depressive disorder and incapacity to work: Preliminary findings of a controlled, matched study.
Bibliographic record
Abstract
The aim of this study was to compare the effectiveness of work-related cognitive-behavioral treatment (W-CBT) with that of cognitive-behavioral treatment as usual (CBT-AU) for employees on sick leave as a result of a major depressive disorder (MDD). We collected data for 26 matched outpatients at pre- and posttreatment, as well as at 1-year follow-up. Outcome measures were the days of incapacity to work (DIW) as well as self-report measures (Beck Depression Inventory [BDI], Symptom Checklist 90-R [GSI], Life Satisfaction Questionnaire [FLZ]). We analyzed data with hierarchical linear modeling in a 2-level model. Therapy effects were defined in 3 ways: effect size (ES), response (based on the reliable change index), and remission compared with the general population's symptom level. The DIW were reduced significantly after both types of treatment, but employees showed even fewer DIW after W-CBT. At follow-up, significantly more employees were working as a result of W-CBT than with CBT-AU. Significant improvements on scores of self-rating measures corresponded with moderate-to-large effect sizes for both treatment types. Approximately 2 thirds of the treated employees were categorized as unimpaired on BDI scores at posttreatment and at follow-up. At least 1 half of the employees were classified as unimpaired on GSI scores at both assessment points. In future research, a randomized controlled trial should be conducted using a larger sample size to investigate the impact of moderators (e.g., employees at different branches of the company). Findings provided support for using common CBT techniques to enhance return to work without losing expected improvements at the symptom level.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".