Developing a Social Autopsy Tool for Dengue Mortality: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Dengue fever is a public health problem in the tropical and sub-tropical world. Dengue cases have grown dramatically in recent years as well as dengue mortality. Colombia has experienced periodic dengue outbreaks with numerous dengue related-deaths, where the Santander department has been particularly affected. Although social determinants of health (SDH) shape health outcomes, including mortality, it is not yet understood how these affect dengue mortality. The aim of this pilot study was to develop and pre-test a social autopsy (SA) tool for dengue mortality. METHODS AND FINDINGS: The tool was developed and pre-tested in three steps. First, dengue fatal cases and 'near misses' (those who recovered from dengue complications) definitions were elaborated. Second, a conceptual framework on determinants of dengue mortality was developed to guide the construction of the tool. Lastly, the tool was designed and pre-tested among three relatives of fatal cases and six near misses in 2013 in the metropolitan zone of Bucaramanga. The tool turned out to be practical in the context of dengue mortality in Colombia after some modifications. The tool aims to study the social, individual, and health systems determinants of dengue mortality. The tool is focused on studying the socioeconomic position and the intermediary SDH rather than the socioeconomic and political context. CONCLUSIONS: The SA tool is based on the scientific literature, a validated conceptual framework, researchers' and health professionals' expertise, and a pilot study. It is the first time that a SA tool has been created for the dengue mortality context. Our work furthers the study on SDH and how these are applied to neglected tropical diseases, like dengue. This tool could be integrated in surveillance systems to provide complementary information on the modifiable and avoidable death-related factors and therefore, be able to formulate interventions for dengue mortality reduction.
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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 | 0.044 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".