Factors Affecting Non-Compliance of Patients Taking Tuberculosis Medication in The Working Area of Health Services of Liquiça District, Timor-Leste, 2018
Bibliographic record
Abstract
Introduction: Non-adherence to taking tuberculosis medication is a patient who is unable or refuses to take tuberculosis medication based on a doctor's prescription, such as: taking inconsistent medication, inadequate clinic visits, inadequate dot program, refusing to take medication. Of the 1/2 million people diagnosed with mdr-tuberculosis, the rate of non-adherence to treatment is difficult to assess and it is estimated that more than a quarter of tuberculosis patients fail to complete treatment within 6 months. Method: The method used quantitatively with the cross-sectional study approach, the population is all tuberculosis patients who run the dots program including those who fail treatment, relapse, and return after defaulted with the age of more than >14 years as many as 142 patients, samples as many as 105. Data collection techniques use nominally scaled questionnaires. Result and Discusion: The test results of univariate and bivariate analysis of chi-square, bivariate analysis using a significant alpha value of 5% (?=0.05). The results of bivariate analysis showed that there was a significant influence between education level (rp = 3,420), knowledge level (rp = 3,052), family support (rp = 0,003), length of treatment (rp = 3,149), on the patient's inadequacy in taking tuberculosis drugs. Conclusion: In conclusion, the results of this study are the level of education, level of knowledge, family support, duration of treatment affect the non-compliance of patients taking tuberculosis drugs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".