Operational research highlights ongoing challenges for comprehensive TB services in Papua New Guinea
Bibliographic record
Abstract
Papua New Guinea (PNG) is a high-burden country for TB, with an estimated annual TB incidence rate of 432 per 100,000 population. There are major challenges to the provision of quality care for TB patients with high rates of loss to follow-up, and multidrug-resistant TB is increasingly detected. In 2022–2023, the second Structured Operational Research Training IniTiative (SORT-IT) for TB was undertaken. Eight participants completed the course, and the outputs from these research projects highlight important current operational issues for the PNG TB programme in a range of settings. The first four articles in the series are published in this issue of Public Health Action, with the remainder to follow in subsequent issues.
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.018 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".