Effect of repetitive feedback on residents' communication skills improvement.
Bibliographic record
Abstract
To evaluate the effect of frequent feedback on residents' communication skills as measured by a standardized checklist. Five medical students were recruited in order to assess twelve emergency medicine residents' communication skills during a one-year period. Students employed a modified checklist based on Calgary-Cambridge observation guide. The checklist was designed by faculty members of Tehran University of Medical Science, used for assessment of students' communication skills. 24 items from 71 items of observational guide were selected, considering study setting and objects. Every two months an expert faculty, based on descriptive results of observation, gave structured feedback to each resident during a 15-minute private session. Total mean score for baseline observation standing at 20.58 was increased significantly to 28.75 after feedbacks. Results markedly improved on "gathering information" (T1=5.5, T6=8.33, P=0.001), "building relationship" (T1=1.5, T6=4.25, P<0.001) and "closing the session" (T1=0.75, T6=2.5, P=0.001) and it mildly dropped on "understanding patients view" (T1=3, T6=2.33, P=0.007) and "providing structure" (T1=4.17, T6=4.00, P=0.034). Changes in result of "initiating the session" and "explanation and planning" dimensions are not statically significant (P=0.159, P=0.415 respectively). Frequent feedback provided by faculty member can improve residents' communication skills. Feedback can affect communication skills educational programs, and it can be more effective if it is combined with other educational methods.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".