Effect of Shanghai's standardized training of residents on the history taking mode of physicians
Bibliographic record
Abstract
Objective To evaluate the needs of performing a standardized communication skill training program for residents according to the differences in history taking mode of residents with different degrees and before and after the standardized training in Shanghai Changhai Hospital in 2010.Methods History taking modes of 81 residents in 2010 before and after the standardized training in Shanghai Changhai hospital were categorized.History taking modes were classified into:no effectiveness mode,traditional mode,disease-sickness mode and Calgary-Cambridge Guide mode.Distribution differences of history taking mode of residents with different medical degrees were analyzed by Fisher exact probability method (α =0.05).Distribution differences of history taking mode of residents before and after standardized training were analyzed by Pearson x2 test (α =0.05).Results 19.8％ residents took no effectiveness mode,53.0％ took traditional mode and 27.2％ used disease-sickness mode.There were significant differences in history taking modes among residents with different medical degrees (P =0.008).After training,history taking modes of residents were significantly changed (P=0.001),only 1.2％ residents used no effectiveness mode,59.3％ used traditional mode and 34.6％ used disease-sickness mode.But residents using the Calgary-Cambridge mode were not increased.Conclusions There are significant differences in history taking modes among residents with different medical degrees.History taking mode of residents changed after standardized training.But some of the residents still use non-optimal history taking modes; therefore a standardized communication skill training program might be needed in the future. Key words: History taking; Standardized training of resident physicians; Communication skills ; Medical education; Evaluation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".