Support of Characteristics, Physical Environmental and Psychological On Quality Of Life Of Patients With DM Type II
Bibliographic record
Abstract
Background: Diabetes mellitus is one of the diseases that ranks high in the list of non-communicable diseases in Indonesia. Factors that can affect quality of life such as physical environment and psychological. Objective: This study aims to examine the relationship between characteristic, physical environment and psychological on quality of life of type II diabetes mellitus patients at the Barombong Public Health Center, Makkasar City. Methodology: This study is a quantitative research with an analytical observational approach using a cross-sectional design. The population size in this study is 578 individuals, sample calculation using the WHO formula yielded a sample size of 385 individuals with predefined exclusion an inclusion criteria. The sampling technique employed is simple random sampling (SRS), and the hypothesis test used is chi-square. Results: Bivariate statistical analysis shows that there is a relationship between quality of life and age (p=0,000), duration of illness (p=0,000), temperature (p=0,000), noise (p=0,000), positive feelings (p=0,000), thinking, learning, and concentration (p=0,000), self-esteem (p=0,000), while variables that are not associated with quality of life are gender (p=0,111), marital status (p=0,228) and social support (p=0,645). Based on logistic regression analysis, it was found that the factors that most influence quality of life are duration of illness (p=0,000) and positive feelings (p=0,000). Conclusion: Length of suffering and positive feelings are the most dominant variables associated with quality of life with a probability level of 99.8%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".