Bibliographic record
Abstract
For four years we have been iteratively evolving MUTI, a rural telehealth system, for hospitals and clinics in a remote rural part of the Eastern Cape in South Africa (Chetty, 2005; Chetty et al., 2003, 2004a; Maunder et al., 2006; Vuza, 2006; Vuza & Tucker, 2004). MUTI enables nurses and doctors to use a wireless Internet Protocol-based communication system to conduct patient referrals, request ambulance services, order supplies and generally keep in contact with one another. The primary community-oriented goal was to prevent unnecessary travel by sick patients from the clinic to the hospital, as transportation in these poverty-stricken and geographically dispersed areas is difficult and expensive for the local inhabitants. We hope that the system can enable nurses at the clinic to learn how to treat a wide range of problems locally by consulting with doctors that they normally do not meet or even speak with. We also hope that the system will lessen the workload for doctors at the hospital. As we expected, integrating new technologies into their everyday work lives is not straightforward or easy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.310 | 0.089 |
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".