Prevalence of pulmonary tuberculosis among correctional services staff in KwaZulu-Natal prisons.
Bibliographic record
Abstract
Background: Prisons and penitentiaries are viewed as environments that act as reservoirs for the transmission of infectious diseases within their confines.This is due to the 'closed' system they operate.The prevalence of these infectious diseases like pulmonary tuberculosis (PTB) among prisoners or inmates has been reported repeatedly in various settings.Aim: To determine the workplace factors that might lead to an increase in the risk of PTB disease among Correctional Facility Workers (CFWs) in prisons in KwaZulu-Natal (KZN)Method: An assessment of the prevalence of PTB disease among the CFWs at three correctional services facilities located within the province was conducted.These were randomly selected from a total of 37 prisons in KZN.Questionnaires were administered to determine work activities that may place CFWs at increased risk for acquiring PTB.Results: There was a response rate of 74.2%, (n=224 participants).Employees had a mean age of 39.2 (SD: 9.2) years and the mean for the number of years worked in the Correctional Facilities was 9.9 years (SD: 6.03).Among the sample, 21.8% had PTB disease during their employment.Nearly a quarter of the CFWs (33 (14.7%)) who had previously been in contact with PTBpositive offenders were diagnosed with PTB while working at the Correctional Facilities.Medical and allied health workers presented with substantially increased (albeit imprecise) risk for acquiring PTB during employment (OR: 19.8 (95% CI: (1.9-202) and OR: 29.4 (95% CI: (2.65-326) respectively) Conclusions: Findings indicate that CFWs are at risk of developing work-related PTB disease.Healthcare workers recorded a higher prevalence than any other job description.This suggests increased exposure among healthcare workers in these correctional facilities.Appropriate interventions are necessary for the protection of these workers.Pre-employment TB screening Yes Yes Yes
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".