1013 Occupational tuberculosis in south africa: are healthcare workers adequately protected?
Bibliographic record
Abstract
Introduction Effective tuberculosis (TB) infection prevention and control (IPC) measures including education and training are crucial in limiting the spread of TB in healthcare settings. We aimed to explore how well HCWs adhere to TB IPC practices, the nature and extent of training related to TB IPC across demographic and occupational factors. Method An interviewer-driven structured survey was conducted among HCWs in a provincial tertiary hospital in Gauteng Province, South Africa. Data were analysed using SPSS version 24. Pearson’s Chi Square test or Fisher’s exact tests checked differences between categorical variables; logistic regression assessed associations between covariates. Results Of the 285 HCWs surveyed, only 43% reported having received training on TB transmission, signs and symptoms; 29.8% of nurses had been trained on the proper use of N95 respirators; only 5% of support workers were trained on mode of transmission; and only 37.2% of all HCWs were aware of a protocol for managing TB patients. Only 56.3% of nurses and 66.7% of doctors reported they always or sometimes wore respirators when managing suspected or confirmed TB cases, although 70.5% of the nurses and 86.7% of the doctors reported that these personal protective equipments were not readily available. Importantly, non-clinical (support) HCWs were more than 7 times more likely to use respirators if trained on their proper use. Discussion Major gaps persist in both availability of respirators and training of HCWs on TB transmission, both factors highly associated with lack of adherence to TB IPC. To protect HCWs, hospital management should ensure availability of respirators as well as effective trainings for all job categories, with particular attention to support staff, who seem to be particularly poorly trained and at high risk of TB.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".