Exploring experiential learning within interprofessional practice education initiatives for pre-licensure healthcare students: a scoping review
Bibliographic record
Abstract
BACKGROUND: Interprofessional collaborative team-based approaches to care in health service delivery has been identified as important to health care reform around the world. Many academic institutions have integrated interprofessional education (IPE) into curricula for pre-licensure students in healthcare disciplines, but few provide formal initiatives for interprofessional practice (IPP). It is recognized that experiential learning (EL) can play a significant role supporting IPP education initiatives; however, little is known of how EL is used within education for IPP in healthcare settings. METHODS: We conducted a scoping review to map peer-reviewed literature describing IPP education initiatives involving EL for pre-licensure students in healthcare disciplines. A literature search was executed in MEDLINE, CINAHL, EMBASE, ERIC, PsycINFO, Scopus, and Social Services Abstracts. After deduplication, two independent reviewers screened titles and abstracts of 5664 records and then 252 full-text articles that yielded 100 articles for data extraction. Data was extracted using an Excel template, and results synthesized for presentation in narrative and tabular formats. RESULTS: The 100 included articles represented 12 countries and IPP education initiatives were described in three main typologies of literature - primary research, program descriptions, and program evaluations. Forty-three articles used a theory, framework, or model for design of their initiatives with only eight specific to EL. A variety of teaching and learning strategies were employed, such as small interprofessional groups of students, team huddles, direct provision of care, and reflective activities, but few initiatives utilized a full EL cycle. A range of perspectives and outcomes were evaluated such as student learning outcomes, including competencies associated with IPP, impacts and perceptions of the IPP initiatives, and others such as client satisfaction. CONCLUSION: Few educational frameworks specific to EL have been used to inform EL teaching and learning strategies to consolidate IPE learning and prepare students for IPP in healthcare settings. Further development and evaluation of existing EL frameworks and models would be beneficial in supporting robust IPP educational initiatives for students in healthcare disciplines. Intentional, thoughtful, and comprehensive use of EL informed by theory can contribute important advances in IPP educational approaches and the preparation of a future health care workforce.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".