Use of Hybrid Methods in Improving Community Healthy Lifestyle Behavior during the COVID-19 Pandemic in Indonesia: Opportunities and Challenges
Bibliographic record
Abstract
BACKGROUND: The COVID 19 pandemic is a challenge for public health services in Indonesia because various priority health services for diabetes prevention in the community have not been implemented. Diabetes risk factors such as obesity, lack of physical exercise, and eating fewer vegetables and/or fruit also increased. The SESAMA (Segitiga Kerjasama/Triangle of cooperation) model is a Diabetes control model whose implementation is carried out directly in the community but during the COVID-19 pandemic, it could not be fully implemented so the hybrid method became a modification of the implemented strategy. This study aims to find out whether the SESAMA model can be implemented using the hybrid method. METHODS: The study was conducted by a survey to all people aged > 18 years in 4 target villages, who participated in fasting blood glucose screening and body weight measurements. The survey produced a number of respondents who met the criteria for obesity with or without prediabetes and were given the SESAMA model of intervention. RESULTS: There was a decrease in the number of people with prediabetes from 148 people to 105 people (29.05%). Prediabetes with obesity also experienced a reduction. From 108 people with prediabetes with grade 3 obesity, 14 people were reduced to 3 people (78.57%); for grade 2 as many as 12 people were reduced to 9 people (25%) and for grade 1 as many as 82 people increased to 83 people and normal weight increased from 40 people to 53 people. CONCLUSIONS: Implementation of the SESAMA Model for people with Prediabetes during the COVID-19 Pandemic which was carried out using the hybrid method showed a decrease in the proportion of people with prediabetes. The proportion of obesity in people with prediabetes has also decreased. The SESAMA model during a pandemic can be implemented using the hybrid method, by maintaining the application of strict health protocols and optimizing cooperation with various stakeholders in the community.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".