Teaching Health Advocacy: A Systematic Review of Educational Interventions for Postgraduate Medical Trainees
Bibliographic record
Abstract
PURPOSE: A systematic review was undertaken to characterize the training approaches that are currently being implemented in postgraduate medical education to teach residents advocacy skills. METHOD: An initial search was conducted in MEDLINE, PubMed, Embase, ERIC, and PsycINFO in November 2016 (updated in December 2017) for articles discussing postgraduate medical education interventions covering advocacy. Articles published between 1995 and 2017 were included. Two authors independently reviewed titles and abstracts (and, if needed, the full text) for inclusion; disagreements were resolved by consensus. Data were extracted from studies to characterize the content and pedagogy of the interventions by mapping them to the CanMEDS health advocate core competencies and key concepts. RESULTS: A total of 3,027 unique abstracts were retrieved; 2,864 were excluded upon title and abstract review, and another 85 were excluded upon full-text review. Seventy-eight total articles were included. More studies involved residents from pediatrics, psychiatry, primary care or preventative medicine, or internal medicine than from emergency medicine, surgery, obstetrics and gynecology, or neurology. Published interventions varied widely by pedagogical approach and assessment method. CONCLUSIONS: Using the CanMEDS framework, this review maps the breadth and nature of postgraduate medical education interventions in health advocacy, with applicability to community organizations, program directors, educators, and administrators working to develop advocacy training interventions. Areas of focus included adapting practice to respond to the needs of or advocacy in partnership with patients, communities, or populations served; determinants of health; health promotion; mobilizing resources as needed; and social accountability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".