“It opened up a whole new world”: An innovative interprofessional learning activity for students caring for children and families
Bibliographic record
Abstract
Fostering classroom environments that optimize learning and prepare students for professional practice in complex work environments requires the use of pedagogies beyond traditional didactic styles. Student centred models support a shift from passive to active learning, improve engagement with course material, and promote critical thinking in the classroom. Educators in professional programs must create opportunities for students to learn team processes that prepare them for their future work environment. The purpose of this project was to explore students’ perception of their learning while participating in a mock team meeting, designed to optimize student engagement with course material and promote interprofessional collaboration and learning. Reflective papers were used to provide student perspectives on their learning during the activity. These papers were then analyzed. Student engagement was highlighted as a significant finding from this active learning pedagogy and other findings suggested the interprofessional team meeting provided multiple learning opportunities such as the chance to practice a professional role, collaborate as a team, discover other perspectives, and learn about interprofessional roles. Students offered suggestions for subsequent versions of the activity and expressed a desire to engage in more interprofessional learning experiences throughout their programs.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".