How have researchers defined institutions, politics, organizations and governance in research related to epidemic and pandemic response? A scoping review to map current concepts
Bibliographic record
Abstract
In recent years, the literature on public health interventions and health outcomes in the context of epidemic and pandemic response has grown immensely. However, relatively few of these studies have situated their findings within the institutional, political, organizational and governmental (IPOG) context in which interventions and outcomes exist. This conceptual mapping scoping study synthesized the published literature on the impact of IPOG factors on epidemic and pandemic response and critically examined definitions and uses of the terms IPOG in this literature. This research involved a comprehensive search of four databases across the social, health and biomedical sciences as well as multi-level eligibility screening conducted by two independent reviewers. Data on the temporal, geographic and topical range of studies were extracted, then descriptive statistics were calculated to summarize these data. Hybrid inductive and deductive qualitative analysis of the full-text articles was conducted to critically analyse the definitions and uses of these terms in the literature. The searches retrieved 4918 distinct articles; 65 met the inclusion criteria and were thus reviewed. These articles were published from 2004 to 2022, were mostly written about COVID-19 (61.5%) and most frequently engaged with the concept of governance (36.9%) in relation to epidemic and pandemic response. Emergent themes related to the variable use of the investigated terms, the significant increase in relevant literature published amidst the COVID-19 pandemic, as well as a lack of consistent definitions used across all four terms: institutions, politics, organizations and governance. This study revealed opportunities for health systems researchers to further engage in interdisciplinary work with fields such as law and political science, to become more forthright in defining factors that shape responses to epidemics and pandemics and to develop greater consistency in using these IPOG terms in order to lessen confusion among a rapidly growing body of literature.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".