Student-Led Environment for Geriatric Interprofessional Education: A Feasibility and Acceptability Study
Bibliographic record
Abstract
Abstract The field of geriatric medicine is multidisciplinary and requires healthcare professionals to work collaboratively to address the complex needs of older adults. Providing students with clinical training opportunities for interprofessional collaboration can help prepare future clinicians for effective interprofessional practice. This study aimed to explore the acceptability and feasibility of a student-led environment in a Specialized Geriatric Clinic through an implementation science lens. Occupational therapy, pharmacy, and social work students collaborated to deliver brain health education to clients and families. Preliminary data demonstrates that the student-led environment is perceived by both interprofessional staff (N = 4) and students (N = 6) as acceptable, with an average rating of 4.3/ 5 on the eight domains of the Theoretical Framework of Acceptability. The domains of perceived effectiveness (4.6), intervention coherence (4.5) and overall acceptability (4.8) received the highest ratings. Perceived burden (3.8) received the lowest rating, highlighting the ongoing barriers these environments pose. Client feedback affirmed the environment’s acceptability, with a satisfaction survey (N = 18) reporting an average rating of 4.3/ 5. Clients found they were treated respectfully (4.9) and had confidence in the students (4.8), however, clients reported that more time could have been spent getting to know them individually (3.5). Operational clinic statistics tracked across the study timeframe found benchmarks for feasibility were met for indicators such as staff workload, number of students placed in the clinic environment, and number of clients who participated. These results will help guide future implementations of student-led environments in this local context and may assist organizations with similar structures.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".