Interprofessional education opportunities and attitudes among counselling psychology students at three Canadian universities
Bibliographic record
Abstract
The purpose of this research was to explore and describe the perceptions and attitudes of graduate counselling students in three universities in Canada regarding interprofessional education (IPE) and collaboration. Understanding how counsellor training programs are preparing students to work collaboratively with other health care professionals was also explored. The data for this study was collected using the Readiness for Interprofessional Learning Scale (RIPLS) that was created by Parsell and Bligh (1999) and adapted by McFadyen, Webster, Strachan, Figgins, Brown & McKechnie (2005). Demographic questions such as age, sex, educational institution attended, year of program, and previous IP experiences and work in an IP environment were also collected. Three additional questions, developed by the research team, which related to perceptions of IP collaboration, were also included in this survey. Sixty-five graduate students (Masters and Doctoral) in the field of counselling psychology participated in this study. The results of this thesis indicated that counselling psychology students value IPE and collaboration. Counselling psychology students indicated that they believed that IPE and collaboration is beneficial to clients and is a crucial factor in delivering quality care. Another major finding indicated that students perceived that they had little opportunities during their graduate education to experience interdisciplinary collaboration. Implications for training and future research are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".