Fostering the development of non-technical competencies in medical learners through patient engagement: a rapid review
Bibliographic record
Abstract
Background: To train physicians who will respond to patients' evolving needs and expectations, medical schools must seek educational strategies to foster the development of non-technical competencies in students. This article aims to synthetize studies that focus on patient engagement in medical training as a promising strategy to foster the development of those competencies. Methods: We conducted a rapid review of the literature to synthetize primary quantitative, qualitative and mixed studies (January 2000-January 2022) describing patient engagement interventions in medical education and reporting non-technical learning outcomes. Studies were extracted from Medline and ERIC. Two independent reviewers were involved in study selection and data extraction. A narrative synthesis of results was performed. Results: Of the 3875 identified, 24 met the inclusion criteria and were retained. We found evidence of a range of non-technical educational outcomes (e. g. attitudinal changes, new knowledge and understanding). Studies also described various approaches regarding patient recruitment, preparation, and support and participation design (e.g., contact duration, learning environment, patient autonomy, and format). Some emerging practical suggestions are proposed. Conclusion: Our results suggest that patient engagement in medical education can be a valuable means to foster a range of non-technical competencies, as well as formative and critical reflexivity. They also suggest conditions under which patient engagement practices can be more efficient in fostering non-instrumental patient roles in different educational contexts. This supports a plea for sensible and responsive interventional approaches.
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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 | 0.030 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| 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".