Evaluation of a Web-based Teaching Module on Examination of the Hand
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of an online module in the development of medical students' clinical hand examination skills. METHODS: We developed a Web-based module to teach examination of the hand to first-year medical students (n = 99) to address the core skills expected in undergraduate medical training in Canada. The module was compared to the standard recommended text and tutor-led teaching using a validated objective structured clinical examination (OSCE) and a written knowledge test. RESULTS: A total of 17 students completed the OSCE from the book-based learning group, 18 from the tutor-led group, and 26 from the online module group. The average total OSCE score was significantly higher for students in the online module group compared to the textbook group (73.2% and 60.5%, respectively; p = 0.003). There was no significant difference between students in the online module and tutor-led groups (73.2% and 69.0%, respectively; p = 0.31). The online module group had a significantly higher mean total knowledge score than the textbook group (8.4 and 5.7, respectively; p < 0.001; maximum score 10) and the tutor-led group (8.4 and 7.4, respectively; p = 0.04). CONCLUSION: Our study provides evidence that a well designed Web-based module, supported by sound educational theory, is an effective tool in the teaching of musculoskeletal examination skills, and provides some advantages over tutor-led teaching in terms of knowledge retention.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".