In the eye of the multiple beholders: Qualitative research perspectives on studying and encouraging quality of TB care in India
Bibliographic record
Abstract
This paper outlines insights qualitative research brings to the study of quality of care. It advocates understanding care as sequential, interpersonal action aimed at improving health and documenting the networks in which care occurs. It assesses the strengths and weakness of contemporary quantitative and qualitative approaches to examining quality of care for tuberculosis (TB) before outlining three qualitative research programs aimed at understanding quality of TB in India. Three case studies focus on the diagnosis level in the cascade of TB care and use qualitative research to examine the clinical use of pharmaceuticals as diagnostics, the development of diagnostic tests, and the role of care providers in the utilization of diagnostic services. They show that 1) care must be understood as part of relationships over time, 2) the presence or absence of technologies does not always imply their expected use in care, 3) physicians' provision of care is often inflected by their perceptions of patient desires, and 4) effective care is not always perfectly aligned with global health priorities. Qualitative methods with a networked perspective on care provide novel findings that can and have been used when developing quality of care improvement interventions for TB.
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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 | 0.112 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.025 | 0.069 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.008 |
| 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".