Public participation in healthcare students' education: An umbrella review
Bibliographic record
Abstract
BACKGROUND: An often-hidden element in healthcare students' education is the pedagogy of public involvement, yet public participation can result in deep learning for students with positive impacts on the public who participate. OBJECTIVE: This article aimed to synthesize published literature reviews that described the impact of public participation in healthcare students' education. SEARCH STRATEGY: We searched MEDLINE, EMBASE, ERIC, PsychINFO, CINAHL, PubMed, JBI Database of Systematic Reviews and Implementation Reports, the Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects and the PROSPERO register for literature reviews on public participation in healthcare students' education. INCLUSION CRITERIA: Reviews published in the last 10 years were included if they described patient or public participation in healthcare students' education and reported the impacts on students, the public, curricula or healthcare systems. DATA EXTRACTION AND SYNTHESIS: Data were extracted using a predesigned data extraction form and narratively synthesized. MAIN RESULTS: Twenty reviews met our inclusion criteria reporting on outcomes related to students, the public, curriculum and future professional practice. DISCUSSION AND CONCLUSION: Our findings raise awareness of the benefits and challenges of public participation in healthcare students' education and may inform future research exploring how public participation can best be utilized in higher education. PATIENT OR PUBLIC CONTRIBUTION: This review was inspired by conversations with public healthcare consumers who saw value in public participation in healthcare students' education. Studies included involved public participants, providing a deeper understanding of the impacts of public participation in healthcare students' education.
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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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".