Development of a competency framework for postgraduate training in obstetrics and gynaecology using a Delphi study
Bibliographic record
Abstract
Objectives: The aim of this study was to create a new integrated competency framework for the postgraduate training in obstetrics and gynaecology and to reach consensus through a Delphi study. Methods: Using the Canadian Medical Education Directives for Specialists (CanMEDS) framework as a basis, three existing frameworks were merged by screening for keywords. Subsequently, consensus on the unified framework was reached through a Delphi study: a group of 18 Belgian experts was asked for their opinions on the competencies through three successive questionnaires. Results: In the first round, one of the in total 91 competencies was deemed irrelevant. In the second round, the competencies were reviewed for content and formulation, after which consensus was not reached on 15 competencies. These 15 competencies were adjusted as needed based on comments collected during the first two rounds. The adjusted competencies were then sent back to the experts in the third round, resulting in a final consensus on all 91 competencies. However, the comments indicated that several competencies were considered broad or vague, casting doubt on their practical applicability. Conclusions: Through a Delphi study, consensus was reached on a newly composed competency framework. Such a holistic competency framework can form the basis of a curriculum reform in the postgraduate training in obstetrics and gynaecology within Belgium, but also in a more international context. Further research is needed to develop an assessment tool to implement these competencies in practice.
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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 | 0.121 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".