An Analysis of Obesity and Associated Health Implications in Colorado and Mississippi States in U.S.A.
Bibliographic record
Abstract
The study tries to investigate the obesity and obesity related health implications of Colorado and Mississippi States to see the significant factors affecting obesity in each state to propose effective and doable policy suggestions to the states, especially to Mississippi state. The study follows logit analysis using Behavior Risk Factor Surveillance Systems (BRFSS) survey data of 2018. The individual data reported for the states were used for the analysis. The statistical package of STATA was used for the analysis. The analytical results show that physical exercise (EXER), number of sleeping hours (SLEP), and education (EDUC) play a major role in combatting obesity. Also, the impact of smoking (SMOK), alcohol consumption (DRNK), and obesity -related diseases ((DISE). The large differences in value between Colorado and Mississippi indicate the significance of these variables and how they could be used in Mississippi. Thus, Mississippi needs to go for efficient and effective policy implications to facilitate more for physical exercises, and education. Both states report that obesity-related illnesses have a significant impact on obesity. Thus, health programs on these diseases would be required to reduce obesity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".