Family Medicine Maternity Care Call to Action: Moving Toward National Standards for Training and Competency Assessment.
Bibliographic record
Abstract
BACKGROUND: Maternity care is an integral part of family medicine, and the quality and cost-effectiveness of maternity care provided by family physicians is well documented. Considering the population health perspective, increasing the number of family physicians competent to provide maternity care is imperative, as is working to overcome the barriers discouraging maternity care practice. A standard that clearly defines maternity care competency and a systematic set of tools to assess competency levels could help overcome these barriers. National discussions between 2012 and 2014 revealed that tools for competency assessment varied widely. These discussions resulted in the formation of a workgroup, culminating in a Family Medicine Maternity Care Summit in October 2014. This summit allowed for expert consensus to describe three scopes of maternity practice, draft procedural and competency assessment tools for each scope, and then revise the tools, guided by the Family Medicine and OB/GYN Milestones documents from the respective residency review committees. The summit group proposed that achievement of a specified number of procedures completed should not determine competency; instead, a standardized competency assessment should take place after a minimum number is performed. The traditionally held required numbers for core procedures were reassessed at the summit, and the resulting consensus opinion is proposed here. Several ways in which these evaluation tools can be disseminated and refined through the creation of a learning collaborative across residency programs is described. The summit group believed that standardization in training will more clearly define the competencies of family medicine maternity care providers and begin to reduce one of the barriers that may discourage family physicians from providing maternity care.
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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 | 0.178 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".