Clinically Relevant Correlates of Accurate Perception of Patients’ Thoughts and Feelings
Bibliographic record
Abstract
The goal was to explore the clinical relevance of accurate understanding of patients' thoughts and feelings. Between 2010 and 2012, four groups of participants (nursing students, medical students, internal medicine residents, and undergraduate students) took a test of accuracy in understanding the thoughts and feelings of patients who were videorecorded during their actual medical visits and who afterward reviewed their video to identify their thoughts and feelings as they occurred (Test of Accurate Perception of Patients' Affect, or TAPPA). Participants' accuracy scores were then correlated with participants' attitudes toward patient-centered care, clinical course background, recall of clinical conversation, evaluations of clinical performance made by preceptors, evaluations of interpersonal skill made by standardized patients in clinical encounters, and independent coding of behavior in a clinical encounter. Accuracy in understanding patients' thoughts and feelings was significantly correlated with nursing students' clinical course experience, clinicians' favorable attitudes to psychosocial discussion, standardized patients' evaluations of medical students' interpersonal skill, independent coding of medical students' patient-centered behavior while taking a social history, and undergraduates' more accurate recall of what an actor-physician said on video. Accuracy in perceiving patients' thoughts and feelings can be objectively measured and is a skill relevant to clinical performance.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".