Interprofessional education and collaborative practice with practicing radiographers: A mixed methods scoping review
Bibliographic record
Abstract
INTRODUCTION: There is increasing evidence substantiating the advantages of Interprofessional Education and Collaborative Practice (IPECP) in healthcare. Despite this, global adoption is still in its infancy. Whilst there has been some recognition of the importance of collaborative practice in healthcare, implementation of IPECP programs remain limited in many countries. METHODS: This scoping review aimed to synthesise global evidence for the implementation and effectiveness of IPECP on practicing radiographers and to further identify the enablers and barriers to the implementation of IPECP within radiography. The JBI guidelines for the conduct of scoping reviews and the PRISMA guidelines for reporting scoping reviews were followed. Databases searched included Medline, CINAHL, Scopus, Embase, Cochrane library, and JBI. Grey literature was searched through Google, Google Scholar, and the ProQuest Dissertations and Theses Global. RESULTS: Following full text screening, 21 articles were included in the review, and data was extracted onto a custom-developed template. IPECP competencies identified in the included studies were mapped against the Canadian Interprofessional Health Collaborative (CIHC) framework of competencies. CONCLUSION: Results indicate that while certain factors such as peer support and interprofessional communication facilitated IPECP, numerous barriers impeded its implementation on a wider scale. IMPLICATIONS FOR PRACTICE: Implications for practice, policy and research include the need to prioritise funding for IPECP initiatives and to establish regulatory frameworks that support interprofessional collaboration.
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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 | 0.058 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".