Advocacy Curricula in Graduate Medical Education: an Updated Systematic Review from 2017 to 2022
Bibliographic record
Abstract
BACKGROUND: Advocacy is an integral component of a physician's professional responsibilities, yet efforts to teach advocacy skills in a systematic and comprehensive manner have been inconsistent and challenging. There is currently no consensus on the tools and content that should be included in advocacy curricula for graduate medical trainees. OBJECTIVE: To conduct a systematic review of recently published GME advocacy curricula and delineate foundational concepts and topics in advocacy education that are pertinent to trainees across specialties and career paths. METHODS: We conducted an updated systematic review based off Howell et al. (J Gen Intern Med 34(11):2592-2601, 2019) to identify articles published between September 2017 and March 2022 that described GME advocacy curricula developed in the USA and Canada. Searches of grey literature were used to find citations potentially missed by the search strategy. Articles were independently reviewed by two authors to identify those meeting our inclusion and exclusion criteria; a third author resolved discrepancies. Three reviewers used a web-based interface to extract curricular details from the final selection of articles. Two reviewers conducted a detailed analysis of recurring themes in curricular design and implementation. RESULTS: Of 867 articles reviewed, 26 articles, describing 31 unique curricula, met inclusion and exclusion criteria. The majority (84%) represented Internal Medicine, Family Medicine, Pediatrics, and Psychiatry programs. The most common learning methods included experiential learning, didactics, and project-based work. Most covered community partnerships (58%) and legislative advocacy (58%) as advocacy tools and social determinants of health (58%) as an educational topic. Evaluation results were inconsistently reported. Analysis of recurring themes showed that advocacy curricula benefit from an overarching culture supportive of advocacy education and should ideally be learner-centric, educator-friendly, and action-oriented. DISCUSSION: Combining core features of advocacy curricula identified in prior publications with our findings, we propose an integrative framework to guide design and implementation of advocacy curricula for GME trainees. Additional research is needed to build expert consensus and ultimately develop model curricula for disseminated use.
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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 | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".