Tuberculosis infection control measures and knowledge in primary health centres in Bandung, Indonesia
Bibliographic record
Abstract
Background Health care workers (HCWs) in low- and middle-income countries (LMICs) continue to have an unacceptably high prevalence and incidence of Mycobacterium tuberculosis infection due to high exposure to tuberculosis (TB) cases at health care facilities and often inadequate infection control measures. This can contribute to an increased risk of transmission not only to HCWs themselves but also to patients and the general population. Aim We assessed implementation of TB infection control measures in primary health centres (PHCs) in Bandung, Indonesia, and TB knowledge among HCWs. Methods A cross-sectional study was conducted between May and November 2017 amongst a stratified sample of the PHCs, and their HCWs, that manage TB patients in Bandung. Questionnaires were used to assess TB infection control measures plus HCW knowledge. Summary statistics, linear regression and the Kruskal–Wallis test were used for analysis. Results The median number of TB infection control measures implemented in 24 PHCs was 21 of 41 assessed. Only one of five management controls was implemented, 15 of 24 administrative controls, three of nine environmental controls and one of three personal respiratory protection controls. PHCs with TB laboratory facilities and high TB case numbers were more likely to implement TB infection control measures than other PHCs ( p=0.003). In 398 HCWs, the median number of correct responses for knowledge was 10 (IQR 9–11) out of 11. Discussion HCWs had good TB knowledge. TB infection control measures were generally not implemented and need to be strengthened in PHCs to reduce M. tuberculosis transmission to HCWs, patients and visitors.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".