Bibliographic record
Abstract
Objective: This paper aims to describe health promotion (HP) research according to HP activities, strategies, target population, and settings, and to explore challenges for HP to reflect principles and values. Methods: A content analysis was employed for all research reports funded by the Korea Health Promotion Foundation from 2005 to 2011. Content analysis was conducted according to the HP activities and strategies as mentioned in the Ottawa Charter, and by target population and setting. Challenges for HP research were explored by priority actions suggested by the International Union for Health Promotion and Education. Results: The total number of research was 384. The most popular topic was on HP actions for reorienting health services, followed by developing personal skills, creating supportive environments, building healthy public policy, and strengthening community actions. Research focusing on enabling strategies was most dominant among the HP strategies, while both advocating and mediating strategies were unlikely to be studied. An even distribution was found across target populations. The most popular setting was communities, followed by workplaces and schools. Conclusion: HP research tends to be anchored on bio-medical, individualized, and behavioral perspectives. A discussion was made to overcome this tendency by employing HP in social sciences theory and methods.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".