Understanding Feedback for Learners in Interprofessional Settings: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Interprofessional feedback is becoming increasingly emphasized within health professions' training programs. The objective of this scoping review is to determine what is known about how learners perceive and interact with feedback in an interprofessional context for learning. METHODS: A search strategy was developed and conducted in Ovid MEDLINE. Title and abstract screening were performed by two reviewers independently. Next, full texts of selected articles were reviewed by one reviewer to determine the articles included in the review. Data extraction was performed to determine the articles' study population, methodologies and outcomes relevant to the research objective. RESULTS: Our analysis of the relevant outcomes yielded four key concepts: (1) issues with the feedback process and the need for training; (2) the perception of feedback providers, affecting how the feedback is utilized; (3) professions of the feedback providers, affecting the feedback process; and (4) learners' own attitude toward feedback, affecting the feedback process. CONCLUSIONS: The learner's perception of interprofessional feedback can be an obstacle in the feedback process. Training around interprofessional feedback should be included as part of interprofessional programs. Research is needed to explore how to address barriers in feedback interaction that stem from misguided perceptions of feedback providers' professions.
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 | 0.021 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".