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Record W7105897649 · doi:10.17605/osf.io/yp7sx

Changes in eating behaviours due to crises, disasters and pandemics: a scoping review

2020· article· W7105897649 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2020
Typearticle
Language
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsFood insecurityLimitingChristian ministryPopulationPublic healthFood supplySocioeconomic statusFood security

Abstract

fetched live from OpenAlex

Hunter L1, Gerritsen S1 and Egli V2 1. School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, New Zealand 2. School of Nursing, Faculty of Medical and Health Sciences, University of Auckland, New Zealand Background Eating behaviours describe how and what people eat. Eating behaviours encompass a broad range of food-related activities, including food choices, eating patterns, food insecurity, and associated mealtime behaviours, such as eating with others and eating while multi-tasking (LaCaille, 2013). An individual’s social environment, physical environment, and broader macroenvironmental determinants such as public policy and socioeconomic status can influence eating behaviours (Larson & Story, 2009; Sallis et al., 2008). Crisis events, including disasters and pandemics–hereafter “crises”–impact the eating behaviours of populations because of added individual stress, agricultural disruption to the food supply chain, increased food insecurity, and potential evacuation and/or job losses that occur as a consequence of the crisis (Deaton & Deaton, 2020; Górnicka et al., 2020; Huang et al., 2016). Crisis events can restrict the ability of populations to engage in healthy eating behaviours. Global and regional crises have severe impacts on food systems, limiting access to foods that are usually eaten (Paci-Green & Berardi, 2015). Disruption of long-distance food supply chains can influence the ability of local food stores to remain stocked and deal with panic buying (Hobbs, 2020; Oscar A. Gómez S., 2013). Food insecurity occurs when there is limited or uncertain availability of nutritionally adequate, safe and culturally acceptable foods (Holben, 2010; Ministry of Health, 2019; Parnell & Gray, 2014). Whilst the degree of food insecurity is often more pronounced in low- and middle- income countries, food insecurity in high-income countries is also of great public health concern (Parnell & Gray, 2014). Food insecurity in high-income countries has been linked to increased obesity rates, poorer health outcomes and increased rates of poor mental health (Utter et al., 2018). Previous crises such as Hurricane Katrina and the global financial crisis (GFC) have been shown to have significant regional and global impacts on eating behaviours and food access, along with other adverse outcomes, such as mass job losses, increased rates of mental illness, and decreased access to basic medical care (Loopstra et al., 2015; Rose et al., 2011; Zane et al., 2010). The current COVID-19 pandemic will have numerous short- and long-term impacts, due in part to the range of approaches each country’s governments are taking. This study aims to investigate the impacts of a range of crises on eating behaviours in order to gain insight on the most effective approaches to disaster management to guide future research and future policy. To the authors best knowledge, this is the first scoping review on this topic. Goals of this study This protocol describes the methods to be used in a scoping review on the impact of crises on population nutrition and eating behaviours in high-income countries, defined as any country with over $12 235 GNI per capita (OECD, 2016). This review will seek to understand and summarise if, how and why eating behaviours change after a crisis. Methods Search strategy Before the search, a table of key terms was developed (Appendix A) to provide a clear understanding of the definitions of all search terms and inclusion criteria. The primary search concepts used for the review were terms associated with ‘crisis’ (pandemic, COVID-19, coronavirus, Ncov-2019, Sars-cov-2, SARS, H1N1 Influenza 2009, Swine flu, Disease outbreak, natural disaster, mass casualty incident, Hurricane Katrina, Hurricane Harvey, bushfire, hurricane, earthquake, cyclone, tsunami, monsoon, drought, tidal wave, typhoon, flood, avalanche, heatwave, volcanic eruption, blizzard, wildfire, terrorism, bioterrorism, school shooting, and mass violence) and terms associated with ‘eating behaviours’ (eating behaviour, eating habits, eating alone, food insecurity, food assistance, food access, dietary pattern, dietary habits, meals, mealtime environments, mealtime behaviour, mealtime characteristics, family mealtimes). An example search strategy can be seen in Appendix B. The databases used to source literature for this review were: Scopus, CINAHL Plus, and PsycInfo. A specialist public health librarian guided the use of databases and MeSH terms in the search where appropriate. Preliminary searches were run several times, and adjustments were made where necessary. Filters were used to limit the results to peer-reviewed journal articles, about humans, written in English, and published between 2000-2020. Further exclusion terms were added to the search string in order to exclude papers focusing on low- or middle- income countries. Additionally, a simple search containing only the primary concepts was run on Google Scholar in order to source any additional articles. All searches were completed in August 2020. Study selection Articles identified through the search strategy went through two stages of exclusion screening. First, the title and abstract of each article were screened by one researcher (LH) for relevance. The inclusion and exclusion criteria were used to determine the relevance of each article methodically. The second stage of study selection included a screening of the full text of the article by LH. The flow chart shown in Figure 1. includes a breakdown of any reason an article was excluded at this stage. If the relevance of any article was unclear, SG and VE discussed with LH to reach consensus. Inclusion criteria Any peer-reviewed journal article that explored the impact that an immediate disaster event had on any food-related issue or behaviour was included. This included eating patterns, food availability or mealtime characteristics. Due to the nature of this scoping review, all population groups were included, as well as articles regarding a wide variety of disaster events, such as Hurricane Katrina, the 2011 Christchurch earthquakes, the Global Financial Crisis, and most recently, the Covid-19 pandemic. Climate change-related events were generally not included as Climate Change is not a defined event, but a long process of change (Crane et al., 2011). However, in circumstances where climate change had been the precursor to an immediate disaster in the natural world, the article was included. For example, climate change has had immediate impacts on the food environment of the Inuit people in Nunatsiavut, Canada. Articles published over the last twenty years were include in order to account for the crises that have occurred since the turn of the millennium. Only articles about high-income countries were included in order limit the scope of the review. There were no limits on study design, though editorial or commentary pieces were excluded. An outline of inclusion and exclusion criteria are attached as Appendix C. Charting the data Descriptive aspects of the included studies will be recorded in a Microsoft Excel spreadsheet: author, date, title, country, type of disaster and type of eating behaviour. The full texts will then be downloaded into NVivo v.12 and undergo deductive and inductive thematic analysis to answer the research question of how times of crises in high income countries affect eating behaviours. Figure 1. Search strategy flow chart Results As shown in Figure 1, the initial search resulted in 508 documents across all three databases. After both stages of screening, 51 articles were selected for inclusion in the review. Detailed results will be submitted for publication in a peer-reviewed journal on or before January 1st, 2021. Discussion The scoping review will provide a comprehensive overview of the literature surrounding eating behaviours and crisis in high income countries. This information will benefit public health and nutrition policymakers, researchers, and service providers. Results may be used to inform future strategies for managing population food access, public health messaging and support for communities during a disaster in high-income countries. The majority of disasters that have occurred in the past have had region-specific impacts. COVID-19 will likely continue to impact contemporary life, including ongoing disruptions to social, economic and eating environments as we continue to experience resurgences of the disease (Gates, 2020). This research aims to provide greater understanding of what research is needed in the future and to synthesise what is already known. References Crane, T. A., Roncoli, C., & Hoogenboom, G. (2011). Adaptation to climate change and climate variability: The importance of understanding agriculture as performance. NJAS - Wageningen Journal of Life Sciences, 57(3), 179–185. https://doi.org/https://doi.org/10.1016/j.njas.2010.11.002 Deaton, B. J., & Deaton, B. J. (2020). Food security and Canada’s agricultural system challenged by COVID-19. Canadian Journal of Agricultural Economics, 68(2), 143–149. https://doi.org/10.1111/cjag.12227 Gates, B. (2020). Responding to Covid-19 — A Once-in-a-Century Pandemic? New England Journal of Medicine, 382(18), 1677–1679. https://doi.org/10.1056/NEJMp2003762 Górnicka, M., Drywień, M. E., Zielinska, M. A., & Hamułka, J. (2020). Dietary and Lifestyle Changes During COVID-19 and the Subsequent Lockdowns among Polish Adults: A Cross-Sectional Online Survey PLifeCOVID-19 Study. Nutrients, 12(8). https://doi.org/10.3390/nu12082324 Harvey, P., Proudlock, K., Clay, E., & Riley, B. (2010). Food aid and food assistance in emergency and transitional contexts. June. Hobbs, J. E. (2020). Food supply chains during the COVID-19 pandemic. Canadian Journal of Agricultural Economics, 68(2), 171–176. https://doi.org/10.1111/cjag.12237 Holben, D. (2010). Position of the

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1430.195

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.

Opus teacher head0.180
GPT teacher head0.428
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it