Experiences of Patients with Mental Illness’ Interactions with Medical Students: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Mental health is a key area for learning within undergraduate medical education. Given the nature of mental illness, interactions may have the potential to uniquely affect patients. This study set out to systematically review studies reporting experiences and perceptions of patients with mental illness' clinical interactions with medical students. This includes which factors encourage patients to interact with medical students and if patients perceive negative and positive effects from these interactions. METHOD: Studies reporting patient experiences of involvement in undergraduate medicine were included. A standardised search of online databases was carried out independently by 2 authors and consensus reached on the inclusion of studies. Data extraction and quality assessment were also completed independently, after which a content analysis of interventions was conducted and key themes extracted. Studies were included from peer-reviewed journals, in any language. RESULTS: Eight studies from 5 countries were included, totaling 1088 patients. Most patients regarded interacting with medical students as a positive experience. Patients described feeling comfortable with medical students, and the majority believed it is important for students to 'see real patients'. Patients described benefits to them as enjoyment, being involved in student education, and developing an illness narrative. CONCLUSIONS: Results suggest that most patients with mental illness want to interact with medical students, and this should be encouraged during student placements. Further research, however, is required to understand in more depth what else can be done to improve the comfort and willingness for patients to interact with students, including barriers to this.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".