Improving Collaborative Practice to Address Offender Mental Health: Criminal Justice and Mental Health Service Professionals' Attitudes Toward Interagency Training, Current Training Needs, and Constraints
Bibliographic record
Abstract
Background: Professionals from the mental health and criminal justice systems must collaborate effectively to address offender mental health, but interprofessional training is lacking. Pedagogical frameworks are required to support the development of training in this new area. To inform this framework, this article explores the readiness of professionals toward interprofessional training and demographic differences in these. It explores expectations of interprofessional training, perceived obstacles to collaborative working, interprofessional training needs, and challenges facing delivery.Methods and Findings: A concurrent mixed methods approach collected data from professionals attending a crossing boundaries interprofessional workshop. Data were collected through a combination of the Readiness for Interprofessional Learning Scale (RIPLS) questionnaire (N = 52), free text questions (N = 52), and focus groups (N = 6). Mental health and criminal justice professionals' attitude toward interprofessional learning were positive (M = 17.81; N = 43). They did not see their own service as insular (M= 4.02; N = 44) and reported strong person centredness (M = 6.07; N = 43). These findings suggest professionals are open to the introduction and implementation of future interprofessional training. There were no significant demographic differences in these attitudes.Conclusions: Professionals raised a range of generic curriculum and educator mechanisms in the development of future interprofessional training, suggesting the transfer of pedagogical frameworks from established interprofessional programs into this new arena is feasible. Context-specific factors, such as offender national policy agendas and the challenges of user involvement for mentally ill offenders, must be taken into account. Greater clarity on multi- versus interprofessional training is still required with this group of professionals.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".