A best evidence systematic review of interprofessional education: BEME Guide no. 9
Bibliographic record
Abstract
BACKGROUND AND REVIEW CONTEXT: Evidence to support the proposition that learning together will help practitioners and agencies work better together remains limited and thinly spread. This review identified, collated, analysed and synthesised the best available contemporary evidence from 21 of the strongest evaluations of IPE to inform the above proposition. In this way we sought to help shape future interprofessional education and maximize the potential for interprofessional learning to contribute to collaborative practice and better care. OBJECTIVES OF THE REVIEW: To identify and review the strongest evaluations of IPE. To classify the outcomes of IPE and note the influence of context on particular outcomes. To develop a narrative about the mechanisms that underpin and inform positive and negative outcomes of IPE. SEARCH STRATEGY: Bibliographic database searches as follows: Medline 1966-2003, CINAHL 1982-2001, BEI 1964-2001, ASSIA 1990-2003 which produced 10,495 abstracts. Subsequently, 884 full papers were obtained and scrutinized. In addition, hand searching (2003-5 issues) of 21 journals known to have published two or more higher quality studies from a previous review. TOPIC DEFINITION AND INCLUSION CRITERIA: Peer-reviewed papers and reports included in the review had to be formal educational initiatives attended by at least two of the many professional groups from health and social care, with the objective of improving care; and learning with, from and about each other. DATA COLLECTION, ANALYSIS AND SYNTHESIS: Standard systematic review procedures were applied for sifting abstracts, scrutinizing full papers and abstracting data. Two members of the team checked each abstract to decide whether the full paper should be read. A third member was consulted over any discrepancies. Similarly, each full paper was read by at least two members of the team and agreement sought before passing it to one member of the team (SR) for data abstraction. Other members of the team checked 10% of the abstraction records. Coding into a Statistical Package for Social Scientists (SPSS) data base led to collection of different outcome measures used in the primary studies via the common metric of an adapted Kirkpatrick's four-level model of educational outcomes. Additionally, a narrative synthesis was built after analysis of primary data with the 3-P model (presage-process-product) of education development and delivery. HEADLINE RESULTS: Government calls for enhanced collaboration amongst practitioners frequently leads to IPE that is then developed and delivered by educators, practitioners or service managers. Staff development is a key influence on the effectiveness of IPE for learners who all have unique values about themselves and others. Authenticity and customization of IPE are important mechanisms for positive outcomes of IPE. Interprofessional education is generally well received, enabling knowledge and skills necessary for collaborative working to be learnt; it is less able to positively influence attitudes and perceptions towards others in the service delivery team. In the context of quality improvement initiatives interprofessional education is frequently used as a mechanism to enhance the development of practice and improvement of services.
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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 | 0.046 | 0.155 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.012 |
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".