CERA: Clerkships Need National Curricula on Care Delivery, Awareness of Their NCC Gaps.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The Society of Teachers of Family Medicine's (STFM) National Clerkship Curriculum (NCC) was created to standardize and improve teaching of a minimum core curriculum in family medicine clerkships, promoting the Triple Aim of better care and population health at lower cost. It includes competencies all clerkships should teach and tools to support clerkship directors (CDs). This 2014 CERA survey of clerkship directors is one of several needs assessments that guide STFM's NCC Editorial Board in targeting improvements and peer-review processes. METHODS: CERA's 2014 survey of CDs was sent to all 137 CDs at US and Canadian allopathic medical schools. Primary aims included: (1) Identify curricular topics of greatest need, (2) Inventory the percent of family medicine clerkships teaching each NCC topic, and (3) Determine if longitudinal or blended clerkship have unique needs. This survey also assessed use of NCC to advocate for teaching resources and collaborate with colleagues at other institutions. RESULTS: Ninety-one percent of CDs completed the survey. Sixty-four percent reported their clerkship covers all of the NCC minimum core, but on detailed analysis, only 1% teach all topics. CDs need curricula on care delivery topics (cost-effective approach to acute care, role of family medicine in the health care system, quality/safety, and comorbid substance abuse). CONCLUSIONS: Single-question assessments overestimate the percentage of clerkships teaching all of the NCC minimum core. Clerkships need national curricula on care delivery topics and tools to help them find their curricular gaps.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".