Bibliographic record
Abstract
In 2013, a new technology, GeneXpert, was introduced in India, which, in addition to testing for TB, could also diagnose whether the detected strain was drug resistant. By detecting the bacterium more effectively than other available tests and simultaneously testing for resistance, GeneXpert promised to reduce the delay in diagnosis and hence ineffective treatments. The new test was introduced to multiple cities via a coalition that included global health funding bodies, the government of India, the World Health Organization, and non-governmental organizations. Despite the concerted effort of the coalition, among formal providers (those trained in biomedicine) in the private sector, the new technology was not adopted as quickly as had been hoped. Examining formal providers' initial responses to the technology's introduction in the city of Patna reveals how the adoption of new technology can be influenced by the particularities of the local medical market such as the availability of diagnostic tests, presence of informal providers, and reputation of formal providers. While protocols and operations might seem standardized across implementation plans, the work that is required to ensure success must take into account the particular role that the market plays from site to site.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".