Health Promotion Model on Preventive Behaviors of Risk Factors for Adults’ Metabolic Syndrome in Ponorogo, Indonesia
Bibliographic record
Abstract
In Ponorogo, there are three risk factors for metabolic syndrome higher than national numbers that are 74% of physical inactivity with 26.1 % of national number, 46.9% of hypertension risk with 26.6% of national number, and 40.6% of weight with 28.9% of national number (Rosjidi et al., 2015). This research aimed at finding out the effects of individual beliefs, social capital and other effects on preventive behaviors of risk factors for adults’ metabolic syndrome. This control case study was employed on October 2019 in Ponorogo Regency, East Java, Indonesia. The dependent variables were preventive behaviors of risk factors for metabolic syndrome. The independent variables were perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy, governmental support, peer support, social capital, imitation, collective efficacy, and outcome expectations. The data was obtained using questionnaires and then analyzed by using a path analysis of 13 strata program. There were direct effects of self-efficacy, perceived benefits, perceived barriers, and perceived severity on preventive behaviors of risk factors for metabolic syndrome. There were indirect effects of perceived susceptibility toward perceived severity, perceived benefits toward perceived barriers, governmental support toward perceived barriers, peer support toward imitation, collective efficacy toward self-efficacy, imitation toward self-efficacy, outcome expectations toward perceived benefits, social capital toward collective efficacy, imitation through toward preventive behaviors of risk factors for metabolic syndrome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".