Medical Student, Medicine Resident, and Attending Physician Knowledge of the Medicare Prescription Drug Modernization and Improvement Act of 2003
Bibliographic record
Abstract
BACKGROUND: The Medicare Prescription Drug Modernization and Improvement Act of 2003 (MMA) will undoubtedly influence health care delivery and affect how physicians practice medicine in the United States. PURPOSE: To evaluate the extent to which medical students, medicine residents, and physicians are informed about key provisions of the MMA. METHODS: Eighty-four attending physicians, 104 medicine residents, and 115 fourth-year medical students at the University of Pennsylvania were surveyed over a 2-week period in February-March 2004. The brief survey instrument consisted of 10 multiple choice questions: 9 questions assessing how well-informed respondents were about the MMA and 1 question assessing their knowledge of general current events. RESULTS: Most respondents (77.8%) either "strongly disagreed" or "disagreed" that they were adequately informed about the MMA. While more than half of all respondents correctly answered the two questions about drug importation from Canada and general current events, a majority did not provide the correct answer to each of the other questions. No significant differences appeared by training. CONCLUSIONS: Attending physicians, medicine residents, and medical students at the University of Pennsylvania were generally ill informed about the MMA. Physician ignorance about important health care legislation continues to be a significant problem. More effective means of educating and informing medical students and physicians at all levels of training about important health policy changes may be warranted.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".