Mapping the interprofessional education landscape for students on rural clinical placements: an integrative literature review
Bibliographic record
Abstract
INTRODUCTION: Interprofessional collaboration and effective teamwork are core to optimising rural health outcomes; however, little is known about the opportunities available for interprofessional education (IPE) in rural clinical learning environments. This integrative literature review addresses this deficit by identifying, analysing and synthesising the research available about the nature of and potential for IPE provided to undergraduate students undertaking rural placements, the settings and disciplines involved and the outcomes achieved. METHODS: An integrative review method was adopted to capture the breadth of evidence available about IPE in the rural context. This integrative review is based on a search of nine electronic databases: CINAHL, Cochrane Library, EMBASE, MEDLINE, ProQuest, PubMed, SCOPUS, Web of Science and Google Scholar. Search terms were adapted to suit those used by different disciplines and each database and included key words related to IPE, rurality, undergraduate students and clinical placement. The inclusion criteria included primary research and reports of IPE in rural settings, peer reviewed, and published in English between 2000 and mid-2016. RESULTS: This review integrates the results of 27 primary research studies undertaken in seven countries: Australia, Canada, USA, New Zealand, the Philippines, South Africa and Tanzania. Despite geographical, cultural and health system differences, all of the studies reviewed were concerned with developing collaborative, interprofessional practice-ready graduates and adopted a similar mix of research methods. Overall, the 27 studies involved more than 3800 students (range 3-1360) from 36 disciplinary areas, including some not commonly associated with interprofessional education, such as theology. Interprofessional education was provided in a combination of university and rural placement settings including hospitals, community health services and other rural venues. The education activities most frequently utilised were seminars, tutorial discussion groups (n=21, 84%), case presentations (n=11, 44%) and community projects (n=11, 44%) augmented by preliminary orientation and ongoing interaction with clinicians during placement. The studies reviewed demonstrate that rural clinical learning environments provide rich and varied IPE opportunities for students that increase their interprofessional understanding, professional respect for other roles, and awareness of the collaborative and interprofessional nature of rural practice. CONCLUSION: This review addresses the lack of attention given to understanding IPE in the rural context, provides Australian and international evidence that initiatives are being offered to diverse student groups undertaking placements in rural settings and proposes a research agenda to develop a relevant framework to support rural IPE. Rural clinical learning environments afford a rich resource whereby health professionals can conceptualise IPE creatively and holistically to construct transformative learning experiences for students. This review develops a case for supporting the development, trialling, evaluation and translation of IPE initiatives that harness the opportunities afforded by rural placements. Further research is required to examine the ways to optimise IPE opportunities in the rural clinical context, including the potential for simulation-based activities, the challenges to achieving sustainable programs, and to evaluate the impact of interprofessional education on collaboration and health outcomes.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.028 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".