Assessment of the Attitudes and Knowledge of Musculoskeletal Medicine Among Medical Students at King Faisal University: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Musculoskeletal conditions are a frequent reason for seeking medical attention. In the United States and Canada, orthopedic injuries constitute about 15-30% of primary care visits. Physicians from various specialties encounter musculoskeletal conditions and manage both, acute and chronic problems, on a daily basis. Considering this, mastery of the fundamentals of musculoskeletal medicine is required for all medical school graduates. In this study, we aim to evaluate the attitudes and knowledge of musculoskeletal medicine among medical students at King Faisal University. Methodology: A cross-sectional study with one-stage sampling technique was conducted among medical students at King Faisal University from February 2021 to May 2021. Results: Our study demonstrated that students possess a lower level of clinical confidence in their ability to perform musculoskeletal clinical examinations compared to pulmonary clinical examinations. Further, they displayed a lower level of clinical confidence in their ability to make musculoskeletal differential diagnoses compared to pulmonary differential diagnoses. Also, their average scores on the basic competency exam did not reach 73.1. Conclusion: The current study evaluated the attitudes and knowledge of musculoskeletal medicine among medical students at King Faisal University. The findings in our study are consistent with the results of other research that indicate medical students are not getting sufficient education in musculoskeletal medicine.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".