Barriers and facilitators to the implementation of individual placement and support (IPS) for patients with offending histories in the community: The United Kingdom experience
Bibliographic record
Abstract
Introduction We aimed to identify the barriers and facilitators to the implementation of a high fidelity individual placement and support service in a community forensic mental health setting. Method In-depth interviews were conducted with clinical staff ( n = 11), patients ( n = 3), and employers ( n = 5) to examine barriers and facilitators to implementation of a high fidelity individual placement and support service. Data was analysed using thematic analysis, and themes were mapped onto individual placement and support fidelity criteria. Results Barriers cited included competing interests between employment support and psychological therapies, perceptions of patients’ readiness for work, and concerns about the impact of returning to work on welfare benefits. Facilitators of implementation included clear communication of the benefits of individual placement and support, inter-disciplinary collaboration, and positive attitudes towards the support offered by the individual placement and support programme among stakeholders. Offences, rather than mental health history, were seen as a key issue from employers’ perspectives. Employers regarded disclosure of offending or mental health history as important to developing trust and to gauging their own capacity to offer support. Conclusions Implementation of individual placement and support in a community mental health forensic setting is complex and requires robust planning. Future studies should address the barriers identified, and adaptations to the individual placement and support model are needed to address difficulties encountered in forensic settings.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".