Overcoming Barriers to Teaching the Behavioral and Social Sciences to Medical Students
Bibliographic record
Abstract
Most U.S. medical schools offer courses in the behavioral and social sciences (BSS), but their implementation is frequently impeded by problems. First, medical students often fail to perceive the relevance of the BSS for clinical practice. Second, the BSS are vaguely defined and the multiplicity of the topics that they include creates confusion about teaching priorities. Third, there is a lack of qualified teachers, because physicians may have received little or no instruction in the BSS, while behavioral and social scientists lack experience in clinical medicine. The authors propose an approach that may be useful in overcoming these problems and in shaping a BSS curriculum according to the institutional values of various medical schools. This approach originates from insights gathered during their attempts to teach various BSS topics at four Israeli medical schools. They suggest that medical faculties (1) adopt an integrative approach to learning the biomedical, behavioral, and social sciences using Engel's "biopsychosocial model" as a link between the BSS and clinical practice, (2) define a hierarchy of learning objectives and assign the highest priority to acquisition of clinically relevant skills, and (3) develop clinical role models through teacher training programs. This approach emphasizes the clinical relevance of the BSS, defines learning priorities, and promotes cooperation between clinical faculty and behavioral scientists.
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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 | 0.030 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| 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".