Toward Competency-Based Training: To What Extent Are We Competency-Based?
Bibliographic record
Abstract
BACKGROUND: Time-based training models in plastic surgery vary in exposure, resulting in low confidence levels among graduates. The evolution of postgraduate medical education into a competency-based model to address these issues requires an understanding of interventions described in the plastic surgery literature to identify gaps and guide creation of assessments to demonstrate competence. METHODS: A systematic search of the MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature, PubMed, and Cochrane databases from inception until December of 2017 was conducted using search terms and synonyms of educational interventions reported in plastic surgery. Full texts were retrieved following filtering and data extracted were related to intervention design and execution, involvement of competency assessment, and educational objectives and alignment to Accreditation Council for Graduate Medical Education competencies and Royal College of Physicians and Surgeons of Canada Canadian Medical Education Directives for Specialists roles. Study quality was assessed using Kirkpatrick's levels of learning evaluation, validity evidence, and the Medical Education Research Study Quality Instrument score. RESULTS: Of the initial 4307 results, only 36 interventions met the inclusion criteria. Almost all interventions aligned to medical knowledge and patient care Accreditation Council for Graduate Medical Education competencies. One-fifth of the interventions involved no assessment of competency, whereas most displayed assessment at the level of design as opposed to outcomes. Quality assessment revealed low levels of learning evaluation and evidence of validity; the average Medical Education Research Study Quality Instrument score was 10.9 of 18. CONCLUSION: A systematic review of educational literature in plastic surgery was conducted to assess the quality of reported educational interventions, and to help guide creating tools that ensure competency acquirement among trainees.
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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 | 0.050 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".