Transference effects on student physicians' affective interactions and clinical inferences in interviews with standardized patients: an experimental study
Bibliographic record
Abstract
This study applied Andersen??s social cognitive paradigm for the experimental\nstudy of transference to the problem of understanding transference effects on the affective\ninteractions and clinical inferences of student physicians with standardized patients. The\ninvestigator designed a 2X2 experimental study in which the independent variables were:\nsource of information for statements about a standardized patient (participant??s own or\nmatched participant??s) and valence of information in statements about the patient\n(positive or negative). Dependent variables were: affect expressed by a student physician\nin videotapes of a medical interview with a standardized patient, as measured by a\nmodified version of the Specific Affect ?? 16 code system (SPAFF-16), and clinical\ninferences by the student physician as measured by the Physician Clinical Inferences\nScale (PCIS) developed by the investigator. Covariates included gender, physician\nverbosity, and intergenerational family relationship variables as measured by the Personal\nAuthority in the Family System Questionnaire ?? Version C (PAFS-QVC). A 2X2\nMANCOVA was conducted, along with hierarchical regressions of gender and PAFSQVC\nvariables as predictors of negative and positive affect and clinical inferences (likelihood of treatment success and patient as partner). One sample of undergraduate\nmedical students (n= 71) provided data for the study.\nResults indicated no statistically significant differences between experimental\ngroups regarding the effect of the experimental manipulation of patient information on\nstudent physicians?? affective interactions and clinical inferences with patients when\ngender, physician verbosity, and related PAFS-QVC variables were controlled.\nHierarchical regression analyses of gender and related PAFS-QVC variables onto\npositive affect, negative affect, clinical inferences (patient as partner) and clinical\ninferences (likelihood of treatment success) revealed statistically significant effects of\nintergenerational family relationship and peer relationship variables on student\nphysicians?? affective interactions and clinical inferences with patients.
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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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".