Pro forma: impact on communication skills?
Bibliographic record
Abstract
BACKGROUND: A doctor performs 160 000-300 000 interviews during a lifetime career, thus making the medical interview the most common procedure in clinical medicine. It is reported that 60-80 per cent of diagnosis is based on history taking, yet there is little published data advising on the best method for medical students to initially attain and further refine these core skills during their medical degree. METHODS: Medical students interviewed two patients: using an open interview first, based on the Calgary-Cambridge approach, and then using a structured pro forma. The students' medical data were assessed by a senior lecturer, and their communication skills were assessed by a behavioural scientist and by the patients. RESULTS: An exact Wilcoxon paired signed rank test was conducted to determine whether there was a difference between the open interview and pro forma methods for history taking and communication skills. The test yielded p-values of 0.0017 and 0.069, respectively, with the pro forma method providing a statistically significantly higher history-taking score and communication score than the open interview method. Subjectively, patients reported the pro forma method as being preferable. CONCLUSION: Medical students in the early years of training benefit from a structured history-taking pro forma to assist them gather an accurate data set without compromising their interpersonal and communication skills.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.016 | 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".