Identifying Prevalence and Characteristics of Behavioral Health Education in Family Medicine Clerkships:
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Many patients with behavioral health disorders do not seek or receive adequate care for their conditions. Among those that do, most will receive care in a primary care setting. To best meet this need, clinicians will need to demonstrate proficiency of behavioral health skills and evidence-based practices. We sought to explore the degree to which these skills are being taught in family medicine clerkships. METHODS: The Council of Academic Family Medicine's (CAFM) Educational Research Alliance (CERA) 2016 survey of clerkship directors (CDs) was sent to 141 CDs at US and Canadian medical schools with a required family medicine run course. CDs were asked about the inclusion of behavioral health topics, tools, and techniques in the clerkship, as well as rating the importance of these items. RESULTS: Eighty-six percent of CDs completed the survey. Mood disorders (81.4%) were most frequently taught, followed by anxiety disorders (77.8%), substance use disorders (74.4%), and impulse control disorders (39.1%). Screening tools and behavioral health counseling skills were less commonly taught. CONCLUSIONS: Many behavioral health topics are not taught universally to all family medicine clerkship students. Gaps exist between what is included in current curriculum and what is recommended by the National Clerkship Curriculum for family medicine. These gaps may represent challenges for improving the care for patients with behavioral health disorders.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".