INFLUENCE OF PATIENT-RELATED FACTORS ASSOCIATED ON TB OUTCOMES AMONG TB PATIENTS IN MOMBASA COUNTY
Bibliographic record
Abstract
Purpose: The purpose of the study was to determine patient-related factors associated with TB outcomes among TB patients in Mombasa County.
 Methodology: Study was conducted in Mombasa County which is one of the 47 counties in Kenya with an urban population of 1,063,854. The study used a cross-sectional research design. The study population was the total number of notified patients with tuberculosis in one quarter in the study area (Mombasa’s health care units) and this was found to be 1207 in the year 2017. It was from this population that a systematic random sample size of 292 patients were interviewed. Quantitative data were analyzed using SPSS version 20 Descriptive statistics frequency (%), mean, and standard deviation were used to express quantitative data. In bivariate analyses, odds ratios (OR) and 95% confidence intervals (CI) for the association between TB treatment outcome and health related factors, institutional factors and patient related factors was done using logistic regression.
 Results: The results revealed that patients who are educated about health are more likely to cure of TB than patients who are not (OR 1.716, 95% CI, 0.35 to 1.48). More so, patients who receive psychosocial support are more likely to get cured than those who don’t receive psychosocial support (OR 4.08, 95% CI, 2.00 to 8.32). The results of the study give evidence to, therefore, conclude patient related factors are critical to TB treatment outcome. 
 Unique contribution to theory, policy and practice: Strengthening TB adherence counselling for patients treated for the first time to improve treatment completion. Counselling service should also be enhanced for patients taking alcohol and those smoking cigarettes as both are associated with less likelihood of completing TB treatment. Implementation of TB work place program through sensitization of company managers on the importance of TB treatment support to reduce chances of loss to follow as sighted by focused group discussions. Patients centred TB services should be provided by all facilities by focusing on the needs of the patients by agreeing on the best time to pick drugs to reduce the risk of loss to follow ups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".