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Record W7017294599

An analysis of Canadian young adults’ eating behaviours towards sustainable food choices

2023· dissertation· en· W7017294599 on OpenAlexfundaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSustainabilityFood choiceConsumption (sociology)Food systemsEating behaviorHealthy eatingClimate changeFood consumption
DOInot available

Abstract

fetched live from OpenAlex

Human health has always been a major concern when it comes to policy design, decision-making, and planning. However, in recent years and with ideas about sustainability gaining traction, planetary health has also been gaining attention from researchers, policy makers and even businesses. There is an inevitable link between human and planetary health. Activities related to food provision and food systems in general are a major determinant of human health and environmental sustainability. The global food system requires a transformation to reduce its adverse impacts on both human and environmental health and to achieve food security. While major improvements have been made in practices related to food production, advances are required from the demand side as well. From the demand side, focusing on food consumption can be a promising approach to alleviate the negative impacts associated with food systems. \nIn terms of sustainable eating behaviours, young adults are a critical population. They often have poor eating habits and habits gained at this stage of life can sustain overtime and become their regular eating habits. Furthermore, given the current global environmental changes, young people will experience stronger consequences from environmental challenges, such as climate change. Therefore, their habits and behaviours, including those associated with how they eat, can have major impacts on their future. This dissertation focuses on the eating habits of young adults ages 18 to 24. \nIn this dissertation, the first study is a quantitative analysis where a Canada-wide survey was conducted among young adults to identify the main individual, environmental, and behavioral factors affecting eating behaviours and to categorize this target population into consumer segments reflecting their eating behaviours. The study found, there were six major factors influencing eating behaviours among young adults in Canada including: (1) beliefs (ethical, environmental and personal), (2) familiarity and convenience, (3) joy and experience, (4) food influencers and Sociability, (5) cultural identity, and (6) body image; the respondents were segmented into six groups based on the importance they attributed to each of the identified factors as follows: (1) the conventional consumer, (2) the concerned consumer, (3) the non-trend follower consumer, (4) the tradition-follower consumer, (5) the indifferent consumer and (6) the ‘eat what you love’ consumer; and, more than half of the population in this study have specific considerations and criteria for their food choices, which distinctly differentiates each segment. \nThe second study is a qualitative analysis where focus groups were conducted among university students to first identify the perceived meaning of sustainable food and sustainable eating, and second, to identify the determinants of sustainable eating behaviours among university students. The study found, university students had a wide range of perceptions regarding defining the attributes of sustainable food, and the aspects of sustainable eating behaviours. In addition to the factors previously presented in the framework by Deliens et al., ‘environmental and social values and beliefs’, ‘campus food’, ‘the pandemic’ and ‘food guides and expert recommendation’ were added as determinants of sustainable eating behaviours. Among all categories, the top two themes mentioned by the participants were food literacy, and campus food (meal plan and university food outlet). Finally, identified personal and environmental factors can motivate or act as a barrier for sustainable and healthy behaviors of university students. \nFinally, in third study I looked at the dietary trends of young adults in Canada and how it has changed from 2004 to 2015. Using the CCHS-Nutrition data, I presented the average diet of a Canadian young adult. Additionally, I looked at the carbon footprint (CF) of the average diet and its changes over the 10-year period. Three dietary trends were identified; first, there was a shift towards the consumption of food that is heavily recommended by Canada’s Food guide; second, there was a shift towards the consumption of food that is considered to have lower CF; and third, protein intake increased and was mainly from animal-based sources for both years with almost identical ratio for animal-based to plant-based protein. The study also identified the overall CF of self-reported diets decreased only slightly in 2015. The identified trends demonstrated that although diets of Canadian young adults are moving towards the right direction (healthy and with lower environmental impact), the shift is not significant and needs major interventions, particularly regarding reducing CF. \nThe research presented in this dissertation has contributed to knowledge and the scholarly literature regarding eating behaviours that support both human health and planetary health. This study also helps with the design and implementation of food-choice interventions underscoring the need for population-specific interventions, emphasis on knowledge translation and highlighting the link between food choices and their environmental impacts such as carbon footprint, and the need for interventions at the campus food environment level present a significant opportunity.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.010
GPT teacher head0.228
Teacher spread0.218 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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