Implementation of the individual placement and support pilot program in Spain.
Bibliographic record
Abstract
OBJECTIVE: This article describes the adoption of Individual Placement and Support (IPS) supported employment between 2013 and 2017 in Catalonia (Spain) in the context of high unemployment and a predominance of traditional preemployment training approaches. It reports the experience of implementing IPS to promote competitive job placement of people with mental disorders. METHOD: The Avedis Donabedian Research Institute (FAD) designed, trained, implemented, and evaluated the project. We used a longitudinal, mixed-methods approach. RESULTS: The demonstration project comprised 7 employment services and 12 ambulatory mental health centers. It followed up programs and participants from October 2013 to December 2017. The project added 1,188 new competitive jobs, increased the rate of competitive employment from 16% to 43%, and improved the fidelity of IPS by 44% on the organizational dimension and by 34% on services dimension. The quality of employment was similar to the overall employment market, with 94% of temporary jobs. The qualitative analysis confirmed several areas of improvement, including the vision of recovery, collaborations between vocational and mental health services, work patterns of practitioners, and views of work as an important treatment. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: A strong leadership team, consistent training, and commitment to model fidelity have established IPS in the pilot region as an important intervention to obtain and maintain competitive employment and recovery for people with a mental health condition. (PsycINFO Database Record (c) 2020 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".