Disadvantage and the experience of treatment for multidrug-resistant tuberculosis (MDR-TB)
Bibliographic record
Abstract
In the present research, we aimed to demonstrate how exploring patients’ treatment experiences may help decision makers better understand and pay attention to social impacts of health interventions. We take multi-drug-resistant tuberculosis (MDR-TB) as a paradigm case of a disease that disproportionately affects people already living with disadvantage and for which treatment itself is extremely burdensome. We conducted a total of 140 in-depth interviews with 53 patients, 56 health care providers, and 31 community members.We found that the burdens of MDR-TB treatment described by respondents fell into two categories: those related to managing the medications (n=77) and those related to other aspects of completing treatment (n=52). Respondents also identified social support (n=121), access to essential goods and services (n=74), personal motivation (n=52), and patient knowledge about the relationship between treatment completion and potential cure (n=44) as factors that may either lighten treatment burdens and facilitate completion or add to treatment burdens and inhibit completion. When asked specifically about preferences for MDR-TB treatment advances, respondents favored a shorter course of treatment (n=52) and fewer pills (n=51) over fewer side effects (n=18). According a pattern analysis applied across the data using the core dimensions of social justice we found that experiencing the side effects of MDR-TB treatment tends uniformly to erode all three dimensions. Our findings demonstrate how systematic collection of data about patients’ lived experience can inform decision-making regarding the social impacts of health interventions in at-risk community living with a high-burden of disease from the perspective of disadvantage.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".