Teaching and Learning Interprofessionally: Family Medicine Residents Differ From Other Healthcare Learners
Bibliographic record
Abstract
AbstractBackground: In recent years, interprofessional education and collaborative patient centred care have been promoted to improve efficiency and quality of healthcare service. Teaching interprofessional education has been challenging. There are fewmature curricula, a lack of standardized teaching approaches, and our healthcare learners are educated in different institutional systems. The objective of this study was to explore how one interprofessional educational initiative impacted different healthcare learners from college and university.Methods and Findings: A day-long interprofessional cognitive behavioural therapy (CBT) workshop was presented to learners from multiple disciplines. Within aframework of collaborative, experiential, and reflective learning, the workshop aimed to promote interprofessional teamwork skills, professional roles, and collaborative behaviours. A mixed-methods design using pre- and post-workshop questionnaires was used to evaluate the effectiveness of the workshop. Significant differences were found between family medicine (FM) residents and healthcare learners of other disciplines in three domains: a) satisfaction with the CBT content area of the workshop, b) attitude toward interprofessional learning and collaboration, and c) the interprofessional learning experience.Conclusions: The results resonate with longstanding, taken-for-granted roles and attitudes in the culture of healthcare. This study invites serious consideration of when best to embed interprofessional education in healthcare curricula, so that learners will come to shape a professional identity that includes interprofessional collaborative care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".