Patients’ Perception on Clinical Training and Informed Consent Towards Medical Students in Jazan Hospitals: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Bedside teaching offers many advantages for medical education. When real patients are involved in the clinical practice, teaching medicine often involves difficult ethical dilemmas so it must be precisely detected and properly dealt with. Objective: to evaluate patients' perspectives on clinical training and informed consent within teaching hospitals in Jazan. Method: This cross-sectional observational study targeted all adult who previously met a medical student. A self-administered questionnaire to assess the patient’s perspective on clinical training and informed consent towards medical students were answered by the participants. T-tests and chi-square tests, along with multiple logistic regression, were used for analysis. Results: 200 participants were selected for this study with a mean age of 32.52 years. 51.3% of the participants were female, and 55.3% of the participants were married. 59.6% of the patients reported that the doctor asked for their permission for the student to be present. Only 31.1 % stated that they felt uncomfortable and 70% of the participants reported that they received more explanation about their illness when medical students were present. Almost all patients felt pleased that they had contributed to the students’ medical education. Conclusion: The research has demonstrated that patients' acceptability of medical students appeared to be influenced by the nature of the interaction between the patient and the student, the education level, and the student-patient gender. In general, most patients were pleased that they were able to help in the students' medical education. In order to enhance the learning process for medical students, clinical tutors must benefit from patients who accept medical students.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".