Interprofessional Education in <scp>C</scp>anada: Addressing Knowledge, Skills, and Attitudes Concerning Intellectual Disability for Future Healthcare Professionals
Bibliographic record
Abstract
Abstract With the deinstitutionalization movement in C anada, healthcare professionals are caring for a greater number of individuals with intellectual disability ( ID ) in their practices, and inevitably require additional training to provide this care. Queen's University has responded to this need and developed an innovative educational course promoting interprofessional education ( IPE ) and interprofessional practice ( IPC ) as it relates to ID curriculum and healthcare provision. This study measured healthcare students’ change in knowledge, skills, and attitudes toward individuals with ID and how it affected their readiness for interprofessional care. Subjects were graduate students from the fields of medicine, nursing, clinical psychology, occupational therapy, and physiotherapy. Course curriculum developed used a blended teaching approach with a combination of online learning, lectures, team‐based problem solving, and client interviews. Evaluation was completed on 247 students, utilizing a pre‐post course questionnaire addressing content areas of knowledge, skills, and attitude, and by analyzing individual professional differences. Significant differences were found, indicating improvements in student knowledge and skills for the majority of disciplines after course participation. A positive trend was found in outcome responses for student attitudinal change, ranging from neutral to positive attributions about individuals with ID . Authors note improvements in student learning and positive attitudinal change following an educational course concerning optimal healthcare and collaborative practice in ID . They propose that an interprofessional blended training curriculum for future healthcare professionals can foster best practice and quality service for this currently underserved population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".