Infection control and tuberculosis in health care workers: an assessment of 28 hospitals in South Africa
Bibliographic record
Abstract
SETTING: Twenty-eight public hospitals in the Free State Province, South Africa. OBJECTIVE: To examine the association between tuberculosis (TB) infection control (IC) scores in Free State hospitals and the incidence of TB disease among health care workers (HCWs) in 2012. DESIGN: A cross-sectional survey and mixed-methods analysis of TB IC policies, practices and infrastructure using a comprehensive, 83-item IC audit and observation tool. RESULTS: As the total IC score increased, the probability of TB in an HCW at that hospital decreased. When adjusted for other covariates in multivariate analysis, if the total score of a hospital increased by one unit, the odds of an HCW having TB decreased by 4.9% (95%CI 0.9-8.8). Significant associations were also seen for the personal protective equipment (PPE) score, where odds decreased by 11.5% (95%CI 1.8-20.1) for each unit increase in score. Administrative score, environmental score and miscellaneous score were not statistically significant in the multivariate model. CONCLUSIONS: These findings reaffirm that overall IC and PPE are essential to protect HCWs from acquiring TB. More attention to TB IC is required to protect the health care workforce and to stop the South African TB epidemic.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".