The Influence of Social Support to the Quality of Life of Tuberculosis Patients in Depok, West Java Province, Indonesia
Bibliographic record
Abstract
The quality of life of Tuberculosis (TB) patients is very important to be considered due to infectious disease is chronic that it can affect quality of life. In order to improve quality of life is by providing social support to TB patients. This study aims to discuss the influence of social support to the quality of life of TB patients. This was a longitudinal study (repeated measurements). Data collection with interviews to respondents using the WHOQOL-BREF and Multidimensional Scale of Perceived Social Support (MSPSS). Data analysis using General Estimation of Equotion. The results showed that social support has a strong influence to the quality of life of TB patients (OR = 7.9); An influential source of social support to improve the quality of life of TB patients were family, friends and significant others. Family support provides the highest contribution with an OR of 19.7; An influential type of social support to improve the quality of life of TB patients were emotional, informational and companionship support. Emotional support provides the highest contribution with an OR of 7.4. Social support to TB patients given at the 5th month of treatment have a positive impact on the quality of life with PAR% was 70%. This study recommends improving the social support to TB patients to increase quality of life of TB patients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".