A Systematic Review on Professional Regulation and Credentialing of Public Health Workforce
Bibliographic record
Abstract
The public health workforce (PHW) counts a great variety of professionals, and how services are delivered differs in every country. The complexity and the diversity of PHW professions also reflect structural problems of supply and demand of PHW in various organizations and health care systems. Therefore, credentialing, regulation, and formal recognition are essential for a competent and responsive PHW to address public health challenges. To ensure comparability of the credentialing and regulation systems for the PHW and to enable its collective action at the macro level in the event of a health crisis, we systematically analyzed documented evidence on the PHW. A systematic review was selected to answer the research questions: (1) what are the most effective aspects and characteristics in identified programs (standards or activities) in professional credentialing and regulation of the PHW and (2) what are common evidence-based aspects and characteristics for the performance standards to support a qualified and competent PHW? The identification of professional credentialing systems and available practices of the PHW was performed systematically using a systematic review of international resources in the specialized literature published in English. The PRISMA framework was used to verify the reporting of combined findings from three databases: Google Scholar (GS), PubMed (PM), and Web of Science (WoS). The original search covered the period from 2000 until 2022. Out of 4839 citations based on the initial search, 71 publications were included in our review. Most of the studies were conducted in the US, UK, New Zealand, Canada, and Australia; one study was conducted in an international context for professional credentialing and regulation of the PHW. The review presents specific professional regulation and credentialing approaches without favoring one of the proposed methods. Our review was limited to articles focused on professional credentialing and regulation of the PHW in the specialized literature published in English and did not include a review of primary PHW development sources from international organizations. The process and requirements are unique processes displaying knowledge, competencies, and expertise, regardless of the field of practice. Continuous education, self-regulatory, and evidence-based approach can be seen as common characteristics for the performance standards on both community and national levels. Certification and regulation standards should be based on competencies that are currently used in practice. Therefore, answering questions about what criteria would be used, what is the process operation, what educational background the candidate should have, re-examination, and training are essential for a competent and responsive PHW and could stimulate the motivation of the PHW.
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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 | 0.017 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".