Developing public health competency statements and frameworks: a scoping review and thematic analysis of approaches
Bibliographic record
Abstract
Abstract Competencies ensure public health students and professionals have the necessary knowledge, skills, values, and behaviours to do their jobs effectively. Public health is a dynamic and complex field requiring robust competency statements and frameworks that are regularly renewed. Many countries have public health competencies, but there has been no evidence synthesis on how these are developed. Our research aim was to synthesize the extent and nature of the literature on approaches and best practices for competencies statement and framework development in the context of public health, including identifying the relevant literature on approaches for developing competency statements and frameworks for public health students and professionals using a scoping review; and, synthesizing and describing approaches and best practices for developing public health competency statements and frameworks using a thematic analysis of the literature identified by the scoping review. We conducted a scoping review and thematic analysis of the academic and grey literature to synthesize and describe approaches and best practices for developing public health competency statements and frameworks. A systematic search of six databases uncovered 13 articles for inclusion. To scope the literature, articles were assessed for characteristics including study aim, design, methods, key results, gaps, and future research recommendations. Most included articles were peer-reviewed journal articles, used qualitative or mixed method design, and were focused on general, rather than specialist, public health practitioners. Thematic analysis resulted in the generation of six analytical themes that describe the multi-method approaches utilized in developing competency statements and frameworks including literature reviews, expert consultation, and consensus-building. There was variability in the transparency of competency framework development, with challenges balancing foundational and discipline-specific competencies. Governance, and intersectoral and interdisciplinary competency, are needed to address complex public health issues. Understanding approaches and best practices for competency statement and framework development will support future evidence-informed iterations of public health competencies.
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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 | 0.298 | 0.391 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.068 | 0.059 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".