Examining the Interplay of Food Literacy, Food Waste, and Diet Quality in Canadian Families
Bibliographic record
Abstract
Optimal nutrition in childhood is necessary to promote healthy growth and development. Within the household, parents play a key role in influencing their children’s eating. Research also suggests that food literacy is key in determining household food waste. However, little is known about parental food literacy’s relation to children’s diet quality and household food waste. This thesis includes four studies that aim to address these research gaps. \n\tStudy 1 explores challenges and strategies in meeting the 2019 Canada’s Food Guide recommendation to “cook more often” among mothers (n = 29) and fathers (n = 11) of children aged 2-12 years old. Challenges reported by parents included time constraints, lack of child and spousal support, and lack of food skills. Strategies identified by parents included planning and preparing ahead of time, using devices and appliances to make cooking more convenient, and receiving help from spouse or child. \n\tStudy 2 uses Structural Equation Modelling to build a model representing food skills of mothers (n = 202) and fathers (n = 116) participating in the Guelph Family Health Study. Results showed that fathers’ ability to plan and adjust meals was associated with higher healthfulness of dinner. \n\tStudy 3 investigates the impact of the COVID-19 pandemic on food preparation behaviours and food waste among families with children aged 9-12 years (n = 19). While total food waste remained consistent, unavoidable food waste increased but avoidable other food waste decreased. Families also reported greater frequency of serving leftovers, meal planning, and managing inventory more efficiently. \n\tStudy 4 investigates the feasibility, acceptability, and preliminary impact of a food literacy intervention targeting household food waste in families with children aged 9-12 (n = 19). Most parents and children reported being satisfied with the intervention and its specific components, and preliminary impact results suggest increases in confidence of mothers to reduce food waste and greater knowledge of best before dates in children. \n\tFindings will inform future family-based food literacy interventions as well as help inform policies pertaining to food literacy and household food waste, such as increasing school food literacy education and improving work-life balance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".