Evaluating the components of social and economic resilience: After two large earthquake disasters Rudbar 1990 and Bam 2003

Knowledge creation elements for enhancing community resilience towards disaster: A Delphi study

Knowledge capacity plays a vital role in building community resilience to disasters. However, the problem is that there is no resilience framework that integrates the knowledge creation process. This article introduces a new framework for increasing community resilience based on knowledge creation theory (KCT). This research aims to define the elements that support the Knowledge Creation for Community Resilience (KCCR) and to gain consensus from experts on these factors. This study was conducted using semi-structured interviews with five panellists and three rounds of Delphi technique to determine the assessment of 26 factors (including six additional factors) that have been identified by experts (30, 18 and 11 experts in rounds I, II and III, sequentially). The data analysis was carried out in several stages, and included Spearman's Rank Correlation Coefficient, consensus appraisal and interrater agreement (IRA) statistical evaluation. The result of the agreement level (AL) analysis shows that the majority of the constructs (96.15%) are in the 'moderate strong' category. This study shows that there is a significant consensus (with IRA index [a wg(1)] ranging from 0.529 to 1), and panellists confirm the significance of all the key constructs. Consensus was gained from experts on seven elements that support the KCCR. This study establishes a systematic, operational and multidimensional KCCR framework that combines the concepts of knowledge creation, community resilience and disaster preparedness. This framework can be used as a qualitative instrument or guidance to build community resilience based on knowledge creation and a quantitative tool for measuring community resilience in facing disasters.

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

BACKGROUND

Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH).

METHODS

Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report.

RESULTS

Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness).

CONCLUSIONS

While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.