Multidrug-resistant tuberculosis treatment programmes insufficiently consider comorbid mental disorders
Bibliographic record
Abstract
The successful treatment of multidrug-resistant tuberculosis (MDR-TB) is a global health priority and a key pillar of the World Health Organization's (WHO's) End TB strategy. There has been significant global investment in diagnostic capabilities in recent years. However, we argue that the mental distress of those with MDR-TB and their families continues to be overlooked by TB programmes. Priorities in the End TB Strategy of 'patient-centred care' and 'patient support' are still to be delivered in practice in many low-income settings, and in particular consideration of mental distress. Our experience of undertaking MDR-TB operational research in China, Pakistan, Bangladesh, Nepal and Swaziland has given us detailed insight into the challenges facing patients, their families, health professionals and wider health systems. We are increasingly concerned that psychosocial support, and particularly support focused on mental health, is being insufficiently addressed in national MDR-TB programmes. We suggest that the presence of comorbid mental disorders reduces treatment adherence. We recommend the trialling within TB programmes of brief screening tools for common mental disorders and the incorporation of principles from the WHO Mental Health Gap Action Programme programme into TB programme treatment guidance. Our work in Nepal also suggests that brief psychological counselling delivered by non-specialist counsellors may be feasible.
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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 | 0.002 | 0.014 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".