CONFERD-HP: recommendations for reporting COmpeteNcy FramEwoRk Development in health professions
Bibliographic record
Abstract
BACKGROUND: Competency frameworks outline the perceived knowledge, skills, attitudes, and other attributes required for professional practice. These frameworks have gained in popularity, in part for their ability to inform health professions education, assessment, professional mobility, and other activities. Previous research has highlighted inadequate reporting related to their development which may then jeopardize their defensibility and utility. METHODS: This study aimed to develop a set of minimum reporting criteria for developers and authors of competency frameworks in an effort to improve transparency, clarity, interpretability and appraisal of the developmental process, and its outputs. Following guidance from the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network, an expert panel was assembled, and a knowledge synthesis, a Delphi study, and workshops were conducted using individuals with experience developing competency frameworks, to identify and achieve consensus on the essential items for a competency framework development reporting guideline. RESULTS: An initial checklist was developed by the 35-member expert panel and the research team. Following the steps listed above, a final reporting guideline including 20 essential items across five sections (title and abstract; framework development; development process; testing; and funding/conflicts of interest) was developed. CONCLUSION: The COmpeteNcy FramEwoRk Development in Health Professions (CONFERD-HP) reporting guideline permits a greater understanding of relevant terminology, core concepts, and key items to report for competency framework development in the health professions.
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 | 0.650 | 0.792 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.029 | 0.021 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.019 | 0.031 |
| Open science | 0.021 | 0.026 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".