Do patients’ comfort levels and attitudes regarding medical student involvement vary across specialties?
Bibliographic record
Abstract
BACKGROUND: Studies on patient comfort with medical student involvement have been conducted within several specialties and have consistently reported positive results. However, it is unknown whether the intrinsic differences between specialties may influence the degree to which patients are comfortable with student involvement in their care. AIM: This is the first study to investigate whether patient comfort varies across specialties. METHODS: A total of 625 patients were surveyed in teaching clinics in Family Medicine, Obstetrics/Gynaecology, Urology, General Surgery, and Paediatrics. Seven patient attitudes and patients' comfort levels based on student gender, level of training, and type of clinical involvement were assessed. RESULTS: Patients in all specialties shared similar comfort levels and attitudes regarding medical student involvement for the majority of parameters assessed, suggesting that findings in this area may be generalised between specialties. Most of the inter-specialty variation found pertained to patient preference for student gender and the genitourinary specialties. CONCLUSION: As there are numerous specialties that have never undergone a similar investigation of their patients, this study has important implications for medical educators in those specialties by supporting their ability to apply the results and recommendations of studies conducted in other specialties to their own.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".