Person-centred care for people with tuberculosis-associated comorbidities: a multi-country qualitative study
Bibliographic record
Abstract
Introduction To contribute to the development of a people-centred global framework for collaborative action on tuberculosis (TB) and comorbidities, a rapid qualitative study on the perspectives of people with lived experience of TB and its associated comorbidities was undertaken. Methods From August to October 2021, TB survivors from high-burden countries, who encountered at least one comorbidity during TB treatment, were interviewed to explore their healthcare experiences and priorities. Thematic analysis drew on a healthcare acceptability model. Results Participants (n=24, 13 women) were treated for drug-susceptible (n=13) or drug-resistant (n=11) TB between 2015 and 2021. They faced diverse comorbidities (mental health and substance use disorders, diabetes, Hepatitis C, lupus and HIV); half of whom reported more than one comorbidity, and all faced socioeconomic hardships. TB diagnosis and treatment exacerbated participants’ comorbidities and, in the absence of integrated support, precipitated mental health challenges. Four healthcare priorities for addressing TB-associated comorbidities were identified: (1) disclosure and early identification of comorbidities, (2) timely and affordable access to care for comorbidities, (3) tailored counselling and peer support and (4) coordinated and consolidated care for TB and comorbidities. Conclusion The syndemic manifestation of comorbidities in people affected by TB calls for a people-centred approach to care that facilitates building of trust with multiple care providers, timely linkages to non-TB programmes, access to integrated diagnosis and treatment, allaying intersecting stigmas and self-shame, and care coordination approaches that correspond to people’s needs and preferences. These healthcare priorities were included in the WHO’s Framework for collaborative action on TB and comorbidities .
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".