"Checking up" on Medical Training Programs and Assessments for Foreign Trained Doctors through Multimodal Analysis
Bibliographic record
Abstract
Studies have investigated the culturally-bound characteristics of active listening across several disciplines, including psychology, business and conflict mediation (Lamiani, et al., 2008).Active listening is also valuable in multicultural and multilingual medical consultations as it improves doctor-patient relationships within a patient-centred care model of practice (Vogel et al., 2018).However, there is a dearth of research regarding the extent to which individual behaviours pertaining to active listening are present in clinical interactions.The present study evaluates multimodal active listening performances of non-native English-speaking medical doctors during Objective Structured Clinical Examinations against ideal models of active listening behaviours.Results indicate that non-native speakers' active listening behaviours differed from the baseline study in a number of verbal and non-verbal areas, the ramifications of which could impact perceptions of doctors' indifference regarding patients' health experience.Explanations for the findings and research and pedagogical applications are offered. Keywords: active listening,
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".