Bibliographic record
Abstract
Objectives: This research was conducted to suggest a recommendation for the Korean credentialing policy of health education specialist as the primary human resource in community health promotion activities from the special group perspective of the Korean Society for Health Education and Promotion. Methods: This research was conducted by the professional focus group discussion and descriptive literature review on health education and promotion. Results: This draft recommendation for Korean credentialing system development of health education specialist was based on the four background reasons for modifying health promotion related acts, for developing better policy of health education credentialing, for keeping the public and ethical responsibilities as the competitive professional society, and for improving health promotion activities in Korea. Theoretical background of the four reasons was Ottawa Charter. We classified three credentialing levels of health education specialist based on health education own competencies, coordiating competencies with environmental factors, and research competencies. Furthermore, we developed 10 major roles and categorized 53 sub-roles based on these competencies above. We recommended 10 classes required to take to become Health Education Specialist. These 10 classes were developed based on the credentialing systems in the United States and Japan. These 10 classes were about health education and promotion methods and strategies not health intervention topics. We also built the draft plan for continuing education to keep KCHES based on the NCHEC in the United States. Conclusions: Further research should be conducted to build better health education specialist credentialing systems modifing current communtiy-based health promotion activities in terms of modifying public regulation, developing KCHEC examination system, protecting job security both in public and private sectors, and creating professionalism in KCHEC.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.021 |
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".