Performance-related allowances within the Malawi National Tuberculosis Control Programme.
Bibliographic record
Abstract
SETTING: National Tuberculosis (TB) Control Programme (NTP), Malawi. OBJECTIVES: To determine the feasibility and effectiveness of performance-related allowances for NTP personnel working at central and regional levels in Malawi. In particular, to determine 1) whether programme staff can complete 6-monthly self-assessment forms related to the tasks they are expected to perform during that period, and 2) whether the NTP can achieve four key programme targets related to case finding, treatment outcome and the sending of sputum specimens for drug resistance monitoring. DESIGN: A descriptive study. RESULTS: For January to June 2003, 25 personnel completed self-assessment forms, and in all cases individual performance was judged satisfactory. For July to December 2003, 21 personnel completed self-assessment forms, and in 20 cases individual performance was judged satisfactory. In the first quarter of 2003, only one target was achieved for the country, and NTP personnel were awarded one quarter of the performance payment. In the third quarter, two targets were achieved and NTP personnel were awarded one half of the performance payment. CONCLUSION: It is feasible to implement performance-related payments for NTP personnel. Ways to routinely introduce such a system for NTP and other staff in the health sector urgently need to be explored.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".