Six steps in the right direction: guiding the development of competency frameworks in healthcare professions
Bibliographic record
Abstract
The development of competency frameworks in healthcare professions is characterised by potentially inadequate descriptions of practice, variable developmental approaches, and inconsistent reporting and evaluating of outcomes. This may be in part due to limited existing guidance, which neglects broader contexts, lacks organising frameworks, and fails to provide guidance on selection of methods. To address such concerns, this paper first outlines a ‘systems thinking’ conceptual framework by which to conceptualise and describe clinical practice when developing competency frameworks. This is achieved through combining Ecological Systems Theory and complexity thinking to identify, and explore the contexts and components of clinical practice. The ‘systems thinking’ conceptual framework is then integrated into a six-step model for developing competency frameworks that synthesises and organises existing advice. The six steps include (1) identify practicalities (e.g. purpose, scope, detail, timeline), (2) identify influencing contexts and factors using ‘systems thinking’, (3) use aligned mixed-methods, (4) translate data into competency frameworks, (5) report processes and outcomes, and (6) plan to evaluate, update and maintain the competency framework. The model provides a logical organising structure of principles to guide assumptions and commitments when developing competency frameworks. Additionally, the model affords the flexibility required when exploring professional practice across varying contexts, and suggests employing mixed methodological approaches that are aligned with purpose and scope. The model acknowledges changing and complex contexts, considers existing guidance, and adds a unique and complementary means to conceptualise and improve the competency framework development process.
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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 | 0.128 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.008 | 0.015 |
| 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".