A qualitative meta-synthesis of facilitators and barriers to tuberculosis diagnosis and treatment in Nigeria
Bibliographic record
Abstract
Abstract Background Despite progress in tuberculosis (TB) control globally, TB continues to be a leading cause of death from infectious diseases, claiming 1.2 million lives in 2018; 214,000 of these deaths were due to drug resistant strains. Of the estimated 10 million cases globally in 2018, 24% were in Africa, with Nigeria and South Africa making up most of these numbers.Nigeria ranks 6th in the world for TB burden, with an estimated 4.3% multi-drug resistance in new cases. However, the country had one of the lowest case detection rates, estimated at 24% of incident cases in 2018 - well below the WHO STOP TB target of 84%. This rate highlights the need to understand contextual issues influencing tuberculosis management in Nigeria. Our synthesis was aimed at synthesizing qualitative evidence on factors influencing TB care in Nigeria. Methods A three-stage thematic meta-synthesis of qualitative studies was used to identify barriers and facilitators to tuberculosis case finding and treatment in Nigeria. A search of eleven databases was conducted. The date of publication was limited to 2006 to June 2020. We analyzed articles using a three-stage process, resulting in coding, descriptive subthemes and analytical themes.Results Our final synthesis of 10 articles resulted in several categories including community and family involvement, education and knowledge, attitudes and stigma, alternative care options, health system factors (including coverage and human resource), gender, and direct and indirect cost of care. These were grouped into three major themes: individual factors; interpersonal influences; and health system factors.Conclusion Case finding and treatment for TB in Nigeria currently depends more on individual patients presenting voluntarily to the hospital for care, necessitating an understanding of patient behaviors towards TB diagnosis and treatment. Our synthesis has identified several related factors that shape patients’ behavior towards TB management at individual, community and health system levels that can inform future interventions.
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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 | 0.094 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".