Level and Predicators of quality of Integrated Disease Surveillance and Response for Infectious Disease in Tigray, Northern Ethiopia: Cross-Sectional Study
Bibliographic record
Abstract
Abstract Background: The health impacts of recent global infectious disease outbreaks have demonstrated the importance of strengthening public health systems. The aim of the study was to assess the level of quality of integrated disease surveillance and response for infectious disease in public health facilities of Tigray, Northern Ethiopia. Methods: the study was facility based cross-sectional. It was conducted from June- July 2018 in 46 health facilities. It has involved mixed method approach both quantitative and qualitative data collection methods. Donabedian input-process-output quality assessment model was used to evaluate the service. The magnitude of the association was considered at p-value of ≤0.05 in multivariable logistic regression analysis using adjusted odds ratio (AOR) at 95% confidence interval (CI). Concurrently, facility surveillance officers were subjected to an in-depth interview autonomously to explore factors for good and bad service quality. Quantitative data were analyzed using SPSS version 21. Use of manual thematic approach was used for qualitative data analysis. Result: The level of the overall quality of IDSR service provision has rendered as good in 6 out of 46(13%) studied health facilities. Two third of studied health facilities were rated as good for input service quality but 34.7% for process service quality. The output service quality was two times better than the overall service quality. Being enrollment of HIT to rapid response team (AOR=7, 95% CI: 1.092- 37.857) and accessing technical guideline to the health facility (AOR=3, 95% CI: 0.399-22.567) were predictor factors for facilitating overall service quality.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".