Using short-message-service notification as a method to improve acute flaccid paralysis surveillance in Papua New Guinea
Bibliographic record
Abstract
BACKGROUND: High quality acute flaccid paralysis (AFP) surveillance is required to maintain polio-free status of a country. Papua New Guinea (PNG) is considered as one of the highest risk countries for polio re-importation and circulation in the Western Pacific Region (WPRO) of the World Health Organization due to poor healthcare infrastructure and inadequate performance in AFP surveillance. The Government of PNG, in collaboration with WHO, piloted the introduction of short-message-service (SMS) to sensitize pediatricians and provincial disease control officers on AFP and to receive notification of possible AFP cases to improve surveillance quality in PNG. METHODS: Ninety six health care professionals were registered to receive SMS reminders to report any case of acute flaccid paralysis. Fourteen SMS messages were sent to each participant from September 2012 to November 2013. The number of reported AFP cases were compared before and after the introduction of SMS. RESULTS: Two hundred fifty three unique responses were received with an overall response rate of 21 %. More than 80 % of responses were reported within 3 days of sending the SMS. The number of reported AFP cases increased from 10 cases per year in 2009-2012 to 25 cases per year during the study period and correlated with provincial participation of the health care professionals. CONCLUSIONS: Combined with improved sensitization of health care professionals on AFP reporting criteria and sample collection, SMS messaging provides an effective means to increase timely reporting and improve the availability of epidemiologic information on polio surveillance in PNG.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".