Effectiveness of individual placement and support for people with severe mental illness in the Netherlands: A 30-month randomized controlled trial.
Bibliographic record
Abstract
OBJECTIVE: Whereas in the U.S. and Canada the Individual Placement and Support (IPS) model has proven to be highly effective in enhancing employment perspectives for persons with severe mental illnesses, the evidence base is less abundant in countries with a different socioeconomic climate. The aim of this study was to examine the effectiveness of IPS in the Dutch socioeconomic context. METHOD: A multisite randomized controlled trial was performed following 151 persons with severe mental illnesses expressing an explicit wish for regular employment, comparing IPS with traditional vocational rehabilitation (TVR). Primary outcome was the proportion of persons who were competitively employed over a period of 30 months. Secondary outcomes were self-reported quality of life, self-esteem and mental health. Additionally, the impact of being engaged in competitive employment on these secondary outcomes was examined. RESULTS: In 30 months, 44% of IPS participants found competitive work, compared with 25% of participants supported by TVR. No direct effect of IPS on mental health, self-esteem or quality of life was found. Being competitively employed before follow-up measurements was significantly associated with an increase in mental health, self-esteem and quality of life. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: This study strongly confirms that IPS is an effective method in helping people with severe mental illnesses find competitive work also in countries characterized by a relatively protective socioeconomic climate putting up unintended barriers to employment. The implementation of IPS on a larger scale seems warranted, and new studies are needed on the mechanisms through which IPS works.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".