Development of obstetric anesthesia core competencies for USA residency programs through a Delphi process
Bibliographic record
Abstract
PURPOSE: The standard for anesthesia residency training in the USA mainly relies on the Accreditation Council for Graduate Medical Education (ACGME) Outcome Project, a framework that lacks specific directives for subspecialties including obstetric anesthesia. We aimed to identify core competencies in obstetric anesthesiology that can be adapted to different residency training programs to help improve the quality of training and accountability of the institutions within the USA. METHODS: We identified a preliminary list of competencies from review of existing competency-based obstetric anesthesia training curricula and practice guidelines. We used a modified Delphi methodology to achieve expert consensus among members of the Society for Obstetric Anesthesia and Perinatology education committee. The panellists were asked to evaluate the importance of each competency using a five-point Likert scale, with consensus after two rounds defined at 80% agreement. The responders were also asked at which level of training each competency should be attained. RESULTS: The Delphi rounds had 75% response rate and derived 94 competencies that were categorized under the six ACGME domains: patient care (38), medical knowledge (45), system-based practice (two), practice-based learning and improvement (five), interpersonal communication skills (two), and professionalism (two). CONCLUSION: We generated a residency training competency list for obstetric anesthesiology through expert consensus. This list can be used by residency training programs to develop a structured competency-based curriculum with tangible milestones, thereby reducing heterogeneity in the standard of training.
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 | 0.097 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".