An enhanced individual placement and support (IPS) intervention based on the Model of Human Occupation (MOHO); a prospective cohort study
Bibliographic record
Abstract
Abstract Background Employment is good for physical and mental health, however people with severe mental illness (SMI) are often excluded from employment. Standard Individual Placement and Support (IPS) is effective in supporting around 55% of people with SMI into employment or education. Current research considers enhancements to IPS to improve outcomes for those requiring more complex interventions. Clinicians need to better understand who will benefit from these enhanced IPS interventions. This study offers a new enhanced IPS intervention and an approach to predicting who may achieve successful outcomes. Methods This prospective cohort study included people with SMI who participated in an enhanced IPS service and had prolonged absence from employment. Secondary data analysis was conducted of data gathered in routine clinical practice. Univariate analysis coupled with previous research and clinical consultation was used to select variables to be included in the initial model, followed by a backward stepwise approach to model building for the final multiple logistic regression model with an outcome of successful or unsuccessful goal attainment (employment or education). Results Sixty-three percent of participants in the enhanced IPS successfully attained employment or education. Significant relationships from bivariate analyses were identified between outcomes (employment or education) and seven psychosocial variables. Adapting Routines to Minimise Difficulties, Work Related Goals, and Living in an Area of Lesser Deprivation were found to be significant in predicting employment or education in the final multiple logistic regression model R2 = 0.16 (Hosmer-Lemeshow), 0.19 (Cox-Snell), 0.26 (Nagelkerke). Model χ2(7) = 41.38 p < .001. Conclusion An enhanced IPS service had a 63% rate success in achieving employment or education, higher than comparable studies and provides an alternative to IPS-Lite and IPS-standard for more complex populations. Motivational and habitual psychosocial variables are helpful in predicting who may benefit from an enhanced IPS intervention supporting people after prolonged absence from employment. Trial registration NCT04083404 Registered 05 September 2019 (retrospectively registered).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".