Tuberculosis and blood-borne infectious diseases: workplace conditions and practices of healthcare workers at three public hospitals in the Free State
Bibliographic record
Abstract
Healthcare workers (HCWs) have increased risks due to continued exposure to patients with infectious diseases, particularly tuberculosis and hepatitis B. This study assessed workplace conditions and practices regarding air- and blood-borne infections in public hospitals in the Free State. Workplace audits were conducted in intensive care, medical wards and casualty departments at three Free State public hospitals. A questionnaire survey was also administered to a targeted 20% stratified quota sample at these facilities. Of the 513 HCWs surveyed, 21.2% reported needle-stick injuries and other body fluid exposure and 19.1% were not adequately protected against hepatitis B. Additionally, 68.3% were never screened for tuberculosis, 54.8% did not wear N95® respirators when needed, only 28.5% washed their gloves and 19.8% did not always wash their hands between caring for different patients. Physicians were at highest risk of needle-stick injuries, were less compliant with hand hygiene, and associated with lower rates of tuberculosis screening, reporting spills and wearing N95® respirators. A significant association was also found between training and screening for tuberculosis, and the use of N95® respirators. The workplace audits highlighted infection control hazards, including the improper use of N95® respirators, a lack of available soap and inadequate availability of sharps containers. There is an urgent need to protect HCWs from workplace hazards. Considerable attention is needed to improve infection control practices by HCWs, and especially physicians. Guidelines and legal frameworks exist. It is time to implement the needed measures.“My experience in getting tuberculosis was horrible, and I know other doctors who have had it much worse (full-blown multidrug-resistant tuberculosis). The system has to do much more to protect us if there are going to be people to provide health care in this country.” – Young doctor with rifampicin-resistant tuberculosis, Free State (February 2013)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".