Using reflective practice in interprofessional education and practice: a realist review of its characteristics and effectiveness
Bibliographic record
Abstract
This article presents the results of a realist review of the use of reflective practice interventions aimed at improving interprofessional education and collaborative practice (IPECP). Reflective practice is recognized as one of the determining factors in health and social service professionals' skills development and maintenance, as well as in the establishment of good collaboration practices. In this respect, it is a key element of interprofessional education (IPE) and its relevance in this field is being asserted more and more strongly. However, few studies have been conducted to document its effectiveness. The purpose of this article is therefore to advance knowledge in this field. Searches in health and social services electronic databases identified six studies presenting reflective practice interventions in IPECP aimed at enhancing collaboration among students or practicing professionals. Analysis provided preliminary answers as to the effectiveness of reflective practice interventions in IPECP, as well as pertinent information on the best methods for achieving effectiveness. It concludes by proposing recommendations designed to change reflective practice interventions in IPECP and by stressing the importance of further research in order to document more fully the effectiveness of reflective practice in IPECP and to identify the most promising intervention methods in this regard.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".