The development of competency frameworks in healthcare professions: a scoping review
Bibliographic record
Abstract
Background Competency frameworks serve various roles including outlining characteristics of a competent workforce, facilitating mobility, and analysing or assessing expertise. Given these roles and their relevance in the health professions, we sought to understand the methods and strategies used in the development of existing competency frameworks. Methods We applied the Arksey and O’Malley framework to undertake this scoping review. We searched six electronic databases (MEDLINE, CINAHL, PsycINFO, EMBASE, Scopus, and ERIC) and three grey literature sources (greylit.org, Trove and Google Scholar) using keywords related to competency frameworks. We screened studies for inclusion by title and abstract, and we included studies of any type that described the development of a competency framework in a healthcare profession. Two reviewers independently extracted data including study characteristics. Data synthesis was both quantitative and qualitative. Results Among 5,710 citations, we selected 190 for analysis. The majority of studies were conducted in medicine and nursing professions. Literature reviews and group techniques were conducted in 116 studies each (61%), and 85 (45%) outlined some form of stakeholder deliberation. We observed a significant degree of diversity in methodological strategies, inconsistent adherence to existing guidance on the selection of methods, who was involved, and based on the variation we observed in timeframes, combination, function, application and reporting of methods and strategies, there is no apparent gold standard or standardised approach to competency framework development. Conclusions We observed significant variation within the conduct and reporting of the competency framework development process. While some variation can be expected given the differences across and within professions, our results suggest there is some difficulty in determining whether methods were fit-for-purpose, and therefore in making determinations regarding the appropriateness of the development process. This uncertainty may unwillingly create and legitimise uncertain or artificial outcomes. There is a need for improved guidance in the process for developing and reporting competency frameworks.
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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 | 0.111 | 0.259 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.049 | 0.033 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| 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".