Preventing Occupational Tuberculosis in Health Workers: An Analysis of State Responsibilities and Worker Rights in Mozambique
Bibliographic record
Abstract
Given the very high incidence of tuberculosis (TB) among health workers in Mozambique, a low-income country in Southern Africa, implementation of measures to protect health workers from occupational TB remains a major challenge. This study explores how Mozambique's legal framework and health system governance facilitate-or hinder-implementation of protective measures in its public (state-provided) healthcare sector. Using a mixed-methods approach, we examined international, constitutional, regulatory, and policy frameworks. We also recorded and analysed the content of a workshop and policy discussion group on the topic to elicit the perspectives of health workers and of officials responsible for implementing workplace TB policies. We found that despite a well-developed legal framework and national infection prevention and control policy, a number of implementation barrier persisted: lack of legal codification of TB as an occupational disease; absence of regulations assigning specific responsibilities to employers; failure to deal with privacy and stigma fears among health workers; and limited awareness among health workers of their legal rights, including that of collective action. While all these elements require attention to protect health workers from occupational TB, a stronger emphasis on their human and labour rights is needed alongside their perceived responsibilities as caregivers.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".