factors related to the event of gastritis disease in kelurahan tanjung pinang, jambi city in 2018
Bibliographic record
Abstract
Non-communicable diseases are a major health problem in developing countries that are experiencing a demographic transition and a decline in lifestyle in their communities. The WHO 2013 World Health Research Agency, conducted a review of several countries in the world, obtained a percentage of gastritis in the world, including Britain 22%, China 31%, Japan 14.5%, Canada 35%, and France 29.5%. Based on data from the Jambi City Health Office from 20 puskesmas the number of gastritis cases was 83.21% and the highest number of gastritis events was at tanjung pinang health center, which was 5.91%. This study was conducted to determine the factors associated with the incidence of gastritis in Tanjung Pinang Village, Jambi City. The research design used was quantitative with a cross sectional approach. The population in this study was the productive age community in the area of Tanjung Pinang which numbered 9,489 people. The sampling technique uses Random Sampling techniques with a total sample of 95 respondents. This research was conducted on September 28, October 16, 2018. Data obtained were analyzed by univariate and bivariate using the Chi-Square test. The results showed that there was a relationship between eating patterns with the incidence of gastritis p-value 0,000 (P-Value <0.05), coffee consumption with the incidence of gastritis p-value 0.027 (P-Value <0.05) and the relationship of stress levels with Gastritis incidence p-value 0,000 (P-Value <0.05). It is expected that respondents and the community will always maintain a regular diet, reduce or limit coffee consumption in a day, and exercise for one hour and relax regularly, recreation/refreshing, share or manage work time properly
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".