Individual, community and health systems factors influencing time to notification of tuberculosis: situating software and hardware bottlenecks in local health systems
Bibliographic record
Abstract
BACKGROUND: Despite several global interventions, tuberculosis (TB) remains a leading cause of death affecting millions of people globally. Many TB patients either have no access to quality care or go undetected by national health systems. Several multilevel factors account for under-detection of persons with TB. This study sought to explore patient-related software, community and health systems software and hardware factors influencing time to notification of TB in Lusaka District, Zambia. METHODS: This was an exploratory qualitative case study that adopted a software and hardware lens of conceptualizing health systems. Data were collected from across three sites - urban and peri-urban areas: Chongwe, Kafue, and Lusaka - within Lusaka Province, Zambia. Sixteen key informants - TB corner nurses, community TB treatment supporters, and TB program managers - were interviewed. Six focus groups were held with TB patients. Data were analyzed using thematic analysis. RESULTS: The study identified factors influencing timely TB notification, categorized into software and hardware elements. Patient-related software elements, including TB knowledge and awareness, and health-seeking behavior, are crucial for prompt notification among TB patients. In the community health system, software elements like social stigma and undesirable community attitudes towards contact tracing, and hardware elements such as unbalanced schedules, excessive workload and limited capacity of community TB treatment supporters contribute to delayed TB notification. In the formal health system, software elements like negative attitudes of health providers towards TB patients and demotivation of TB staff, and hardware elements such as high diagnostics and transportation costs, outdated diagnostics in primary care facilities, and slow referral mechanisms, can also delay TB notification. CONCLUSION: Delays in time to TB notification are influenced by a combination of software (attitudinal and behavioral) and hardware (resource-related) elements across TB patients, community health systems, community TB treatment supporters, health providers, and TB staff. Addressing these factors, particularly social stigma, negative attitudes, and resource constraints, is crucial to improving timely TB detection and treatment.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".