Why healthcare workers are sick of TB
Bibliographic record
Abstract
Dr Thato Mosidi never expected to be diagnosed with tuberculosis (TB), despite widely prevalent exposure and very limited infection control measures. The life-threatening diagnosis of primary extensively drug-resistant TB (XDR-TB) came as an even greater shock. The inconvenient truth is that, rather than being protected, Dr Mosidi and thousands of her healthcare colleagues are at an increased risk of TB and especially drug-resistant TB. In this viewpoint paper we debunk the widely held false belief that healthcare workers are somehow immune to TB disease (TB-proof) and explore some of the key factors contributing to the pervasive stigmatization and subsequent non-disclosure of occupational TB. Our front-line workers are some of the first to suffer the consequences of a progressively more resistant and fatal TB epidemic, and urgent interventions are needed to ensure the safety and continued availability of these precious healthcare resources. These include the rapid development and scale-up of improved diagnostic and treatment options, strengthened infection control measures, and focused interventions to tackle stigma and discrimination in all its forms. We call our colleagues to action to protect themselves and those they care for.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".