The impact of social conditions on patient adherence to pulmonary tuberculosis treatment
Bibliographic record
Abstract
SETTING: Tuberculosis (TB) remains one of the main concerns in global health. One of the main threats to treatment success is patient non-adherence to anti-tuberculosis treatment. OBJECTIVE: To identify the relation between social conditions and treatment adherence in a prospective cohort setting in an intermediate TB burden country. DESIGN: To identify associations between poor adherence and social conditions, including educational level, type of residence and occupation, we constructed hierarchical logistic regression models. RESULTS: A total of 551 participants were included in the study. Low educational levels, poor housing and occupations in the construction and manufacturing industries and service sectors were associated with poor adherence; this association was likely to be differentiated by previous history of anti-tuberculosis treatment. CONCLUSION: Policy making should focus on improving the social conditions of patients by working towards better housing conditions and providing health promoting working conditions to enable treatment adherence.
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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 | 0.000 | 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 | 0.000 | 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".