An Effective Teaching Method to Enhance History-Taking Skills for Chinese Medical Students
Bibliographic record
Abstract
History taking is an extremely important skill for medical students to master. In China, medical students usually have opportunities to practise this skill on real patients after they have learned diagnostics and basic relevant theoretical knowledge. Today, however, several factors, such as increased enrolment of medical students and the need to ensure patient safety in avoiding stressful doctor-patient relationships may increase both the difficulty and the importance for medical students to develop this skill. In view of these situations, the aim of this study was to introduce one specific teaching method, i.e., role-play activity, in order to help medical students cultivate and practise history-taking and related skills. 52 third-year medical students were divided into two groups. Students in observation group received role-play activity training before interviewing with real patients. Students in control group were taught by traditional methods without the new method intervention. The teaching effects of role-play activities were evaluated via medical records, tests of history taking and theoretical exams, and questionnaire for the observation group. The scores of seven medical case records for each student in the observation group were analysed and were found to be higher than those in the control group. These results showed no significant differences between the two groups in the first and second interview records with real patients in the hospital, but statistically significant differences were found from the third time. The scores on history-taking tests with a standardized patient (SP) were higher in the observation group than in the control group. No significant difference was found between the two groups in their theory exam scores. Results indicated that role-play activity is an effective method for medical students to improve their history-taking skills.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".