Current trends in the educational approach for teaching interviewing skills to medical students.
Bibliographic record
Abstract
Research in the acquisition of patient interviewing skills by medical students has dealt mostly with the evaluation of the effectiveness of various teaching programs and techniques. The educational approaches (i.e., the tutor-learner relationship and learning atmosphere) have rarely been discussed. These approaches may be grouped into: a) "teacher-centered" (didactic), in which the students are passive recipients of instruction; b) "learner-centered," in which the tutor functions as a facilitator of small group learning, whose task is not to teach but rather to ensure that all students participate in the discussions and share knowledge with other students; and c) "integrated learner-and teacher-centered" or "experiential learning," which consists of an ongoing dialogue between the tutor and the students. In this paper, we review the strengths and weaknesses of these educational approaches and attempt to identify the current trends in their use in the teaching of interviewing skills. It would appear that until the 1960s, medical students acquired interviewing skills without any expert guidance. On the other hand, since the 1970s, there has been a tendency to offer and upgrade undergraduate programs aimed at imparting communication skills to medical students. Initially, these programs were didactic; however, during the last decade, there has been an increasing shift to teaching interviewing skills by promoting experiential learning.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".