A Systematic Review of the Outcome Measures Used to Evaluate Interprofessional Learning by Health Care Professional Students During Clinical Experiences
Bibliographic record
Abstract
Interprofessional education (IPE) occurs when members of more than one health or social care profession learn interactively together to improve interprofessional collaboration and health care delivery. Interprofessional experiences provide students with the skills and knowledge needed to work in a collaborative manner; however, there is no review on the outcome measures used to assess the effectiveness of IPE learning. The current systematic review examined the outcome measures used to assess interprofessional learning during student clinical experiences. An electronic search of databases retrieved trials of health professional students who completed an IPE intervention during a student clinical experience. Methodological quality of twenty-five studies meeting the inclusion criteria published between 1997 and 2018 was scored independently by two raters using the Physiotherapy Evidence Database and the Confidence in the Evidence from Reviews of Qualitative Research tool. The Interdisciplinary Education Perception Scale was used most frequently to assess interprofessional learning during a student clinical experience. This review provides a summary of outcome measures for educators to consider for evaluation of interprofessional activities during student clinical placements and serves to inform future conversations regarding the use and development of outcome measures to provide evidence for student achievement of IPE objectives and competencies.
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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 | 0.023 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".