Individual placement and support versus individual placement and support enhanced with work-focused cognitive behaviour therapy: Feasibility study for a randomised controlled trial
Bibliographic record
Abstract
Introduction Employment is a key goal for many people with long-term mental health issues. Evidence-based individual placement and support is a widely advocated approach. This study explored whether individual placement and support outcomes could be enhanced with work-focused counselling. Method The study was designed as a pragmatic randomised controlled trial comparing the cost-effectiveness, in severe mental illness, of work-focused intervention (intervention) as an adjunct to individual placement and support compared to individual placement and support alone (control). Results The original sample (330) proved impossible to attain so the design was revised to a pilot study from which information on feasibility of a full trial could be drawn. Twenty-five individuals out of 74 found paid work but no difference was found in the mean number of hours in paid employment between the intervention and control groups. Conclusion Results demonstrate that delivering work-focused counselling in tandem with individual placement and support is feasible and acceptable to service users. The study observed that, even during a period of recession (2010–13), individuals with mental health problems succeeded in obtaining paid employment. Any additional benefit of counselling over individual placement and support alone could not be ascertained, due mainly to the high drop-out rate from this study.
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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 | 0.037 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".