Adding Cognitive Remediation to Employment Support Services: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: Individual placement and support (IPS) is an evidence-based strategy that helps individuals with mental illness obtain and maintain competitive employment. Despite the approach's overall success, almost half of IPS clients do not find work. Impairment in cognitive abilities may hamper employment and limit the benefits from rehabilitation services such as IPS. This randomized controlled trial aimed to assess the effects of adding cognitive remediation therapy (CRT) for IPS clients who had difficulties finding employment. METHODS: At 14 mental health centers in Canada, 97 clients who had not found work after 3 months of receiving IPS services were recruited. Consenting clients were randomly assigned to either continue IPS alone or receive CRT added to IPS. The CRT used the Thinking Skills for Work protocol, a 12-week program that included computerized cognitive exercises along with coping strategies for managing cognitive challenges. RESULTS: Participants completed on average 10 of 12 individual training sessions in coping strategies and 12 of 24 computerized training sessions. The addition of CRT to IPS resulted in significantly more participants working at the 3-month (odds ratio [OR]=2.83, 95% confidence interval [CI]=1.22-6.60) and 9-month follow-ups (OR=2.91, 95% CI=1.27-6.65). Participants who received CRT worked more hours and earned more in wages than those receiving IPS alone over the 9-month follow-up period. Both groups showed significantly improved cognitive outcomes at the 3-month follow-up, with no time × group interaction. CONCLUSIONS: Cognitive remediation, especially skills training in coping and compensatory strategies, improves employment outcomes among individuals who do not show an early benefit of using IPS services.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".