Faculty Rating of Learning Objectives for an Undergraduate Medical Curriculum in Substance Abuse
Bibliographic record
Abstract
The purpose of this study is to describe medical faculty's ratings of learning objectives related to substance abuse. A comprehensive set of learning objectives was drafted. The Associate Dean at each of Ontario's five medical schools was asked to select two faculty members from each clinical discipline who were involved in undergraduate medical education. The selected faculty were sent a survey asking them to rate 282 objectives according to their importance for undergraduate education in their discipline, using a 5-point scale. Sixty-eight out of 90 surveys were returned. For statistical analysis, disciplines were placed into two groups, Group 1 (internal medicine, surgery, emergency medicine, and anesthesia) and Group 2 (family medicine, psychiatry, and pediatrics). The mean ratings of Group 1 were significantly higher than Group 2 (p < 0.001) for five sets of objectives: attitudes, epidemiology, screening and assessment, nonmedical interventions, and specific populations (women, the elderly, and adolescents). Group 1 gave mean ratings above 4 to all themes except epidemiology, inpatient care, and medical complications. In contrast, Group 2 gave mean ratings above 4 to only three themes: physician substance abuse problems, withdrawal, and medical complications. The marked differences in learning objectives between disciplines suggest that a discipline-specific approach is needed for curricular development in substance abuse.
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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 | 0.040 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".