Why is an Integrated Social-Ecological Systems (ISES) Lens Needed to Explain Causes and Determinants of Disease? A Case Study of Dengue in Dhaka, Bangladesh
Bibliographic record
Abstract
The purpose of this chapter is to offer reflections on conventional theories concerning causes and determinants of diseases. It also intends to examine both theoretical and empirical bases for adopting an Integrated Social-Ecological Systems (ISES) lens as a tool for understanding complexities related to drivers, determinants and causes of diseases.,We assessed the theoretical underpinnings of a range of historical and contemporary lenses for viewing infectious disease drivers and the implications of their use when used to explain both personal (i.e. individual) and population health. We examined these issues within the empirical context of the City of Dhaka (Bangladesh) by adopting an ISES lens. Within this study an emphasis has been placed on illustrating how feedback loops and non-linearity functions in systems have a direct bearing upon various aspects of infectious disease occurrences.,A brief triumph over microbes during the last century stemmed in part from our improved understanding of disease causation which was built using disciplinary-specific, monocausal approaches to the study of disease emergence. Subsequently, empirical inquiries into the multi-factorial aetiology and the ‘web of causation’ of disease emergence have extended frameworks beyond simplistic, individualistic descriptions of disease causation. Nonetheless, much work is yet to be done to understand the roles of complex, intertwined, multi-level, social-ecological factors in affecting disease occurrence. We argue, a transdisciplinary-oriented, ISES lens is needed to explain the complexities of disease occurrence at various and interacting levels. More theoretical and empirical formulations, with evidence derived from various parts of the world, is also required to further the debate.,Our study advances the theoretical as well as empirical basis for considering an integrated human-nature systems approach to explaining disease occurrence at all levels so that factors at the individual, household/neighbourhood, local, regional and global levels are not treated in isolation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".