The Concurrent Validity of Using Simulated Patient and Real Patient in Communication Skills Assessment of Medical Students
Bibliographic record
Abstract
Background & Objective: Communication skills assessment requires the use of valid instruments. The present study has done to investigate concurrent validity of the simulated patient meaning relationship between test results by the simulated or real patient and possibility to generalize the results by simulated patient and in simulated environment for real patient and in bedside. \nMethods: In this correlation study, 32 medical externships were enrolled by purposive sampling method. The students were divided randomly into two equal and homogeneous groups. Using crossover design, first group were assessed by real patient initially and then, simulated patient and second group, were measured conversely. Communication skills assessment was done using a checklist retrieved from Calgary-Cambridge interview skills checklist. The data were analyzed using descriptive and analytical statistics. \nResults: In first assessment, comparing scores by simulated and real patient in first group, second group and total students showed significant differences. In second assessment between two groups, the statistics results was not significant with very minor differences (P = 0.064). There were positive correlation between scores of total students by simulated and real patient (r = 0.63). \nConclusion: According to the results, communication skills of each student in simulated position cannot be generalized to real situation and cannot be claimed that simulated patient can be used instead of real patient. \n \n \nKeywords \nCommunication skills Assessment Concurrent validity Simulated patient Real patient
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".