Social Support and Treatment Outcomes Among Multi-Drug Resistant Tuberculosis and Persons Living With HIV in Nigeria
Bibliographic record
Abstract
An estimated 40% of people with tuberculosis (TB) are missed due to underdiagnosis, and among those previously treated TB cases, about a quarter develop drug-resistant TB in Nigeria. The emergence of multi-drug-resistant TB (MDR-TB) is stalling the effort towards TB’s control and eradication. Few studies have examined the impact of the psychosocial effect and support systems for TB, particularly for MDR-TB. In this quantitative, cross-sectional study, the predictive relationship between social support given to MDR-TB patients and their treatment outcomes was examined using secondary data of 594 MDR-TB cases enrolled between January 2018 and December 2021. The theoretical framework for this study was grounded in the social-ecological model. Multiple logistic regression was used to determine if a statistically significant predictive relationship exists between treatment outcome and social support while adjusting for sociodemographic factors (age, gender, place of residence, education, marital status), HIV status, and antiretroviral therapy (ART) status among MDR-TB, and persons with HIV in Nigeria. After adjusting for the sociodemographic factors and HIV status, the result was indicative of a statistically significant 728% increased odds of the likelihood of reporting favorable treatment outcomes for MDR-TB among those with social support compared to those without in Nigeria (aOR= 7.277, 95% CI= (1.369 – 38.679), p = 0.02). The sample was limited in size among those with HIV, and as such, the results cannot be replicated due to sparse data. This finding justifies the need to ensure social safety nets and upscale the quality of social support services in a patient-centered approach to meet the needs of the vulnerable population for positive social change.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".