Evaluation of an Interprofessional Educational Intervention in Mental Health and Intellectual and Developmental Disability for Health and Social Service Trainees
Bibliographic record
Abstract
OBJECTIVE: Adults with intellectual and developmental disabilities (IDD) experience high rates of poor mental health and challenges accessing timely and high-quality services. There is limited interprofessional training on mental health care for this population. METHODS: A virtual, synchronous program based on the Project Extension for Community Healthcare Outcomes (ECHO) Ontario IDD Mental Health program was developed for health and social service trainees. Participants represented 10 disciplines across 12 Canadian university or college programs. The program was taught by a team of health and social service providers together with individuals with lived experience and included didactics and case-based discussions. Program evaluation utilized a pre-, post-, and 12-week follow-up survey design with feedback surveys following each session. RESULTS: Fifty participants registered for the program; 34 (68%) completed baseline measures and attended at least two sessions. Overall, participants reported high session satisfaction (average rating of 4.47 of 5). Participants demonstrated significant improvement in self-efficacy regarding communication (p < 0.001), management of mental health needs (p < 0.001), and working across systems (p < 0.001). Participants self-reported feeling more knowledgeable about common comorbidities (p < 0.001), assessing behavioral challenges (p < 0.001), the role of interdisciplinary professionals (p < 0.001), and community resources (p < 0.001). Improvements were maintained at follow-up across outcomes. CONCLUSION: The pilot Project ECHO for health and social service trainees in adult IDD mental health demonstrated high participant satisfaction and positive impact on trainees' self-efficacy and knowledge. Interprofessional educational interventions can be effectively implemented using virtual technology to teach about other mental health populations requiring multisector care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".