Ethical issues in the managed care setting: a new curriculum for primary care physicians
Bibliographic record
Abstract
Several challenging ethical issues have been associated with the shift to managed healthcare in the United States. Our objective was to develop, implement, and evaluate a curriculum designed to help physicians identify and examine ethical issues encountered in the managed care setting. The curriculum was developed during a year-long workshop at Johns Hopkins Bayview Medical Center. The content of the curriculum was established through literature review, focus group discussions with physicians, and a needs assessment of targeted learners (primary care physicians practicing in managed care settings). Some of the key issues addressed in the curriculum include: changing professional responsibilities of physicians; fair use of resources; and threats to the doctor-patient relationship as a consequence of the new healthcare delivery system. The 7.5-h curriculum was taught over five sessions using varied teaching methods. Evaluations demonstrated that the curriculum was successful in increasing learner awareness of ethical issues confronted in the managed care environment and improved learner knowledge in these areas. The physician-learners reported that this educational experience would change their teaching of medical students and residents. After completing the curriculum, learners felt that they were at least somewhat better able to cope with ethical challenges encountered in the managed care setting. Future research might examine whether such a curriculum could positively affect physician behavior or enhance physician satisfaction with the managed care setting.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".