Determinants and public health effects of food cost and availability in two neighborhoods of Hamilton, Ontario
Bibliographic record
Abstract
Diet can powerfully shape human health. Biocultural theory suggests that variation in the cost and availability of food is one factor that affects the health of individuals of differing affluence in dissimilar ways. Though appreciation for the importance of social determinants of healthy eating is growing, very few Canadian studies address links between economic and nutritional variation. To partially address this knowledge gap, this thesis employs a mixed-methods approach including mapping, interviews, face-to-face surveys, and surveys of food cost and availability to investigate whether the cost and availability of food varies between socioeconomically distinct areas of Hamilton, Ontario, and how these differences, if they exist, might differently influence public health in the two areas. Food cost was not found to vary between the two areas, though the availability of food, especially produce, differed. It is suggested that reduced food availability, along with lower incomes and reduced access to transportation, combine to make purchasing foods consistent with a healthy diet more difficult in the less-affluent study area. Interviews with public health workers suggest that this, in conjunction with divergent shopping habits, negatively influences public health in the less-affluent area, but robust quantitative public health data to support or disprove this assertion are lacking at present. As low-income is a strong determinant of inadequate diets, economic approaches designed to make healthy diets more affordable for and readily available to lower-income Canadians are discussed. Links between economics and nutrition are complex; future research into the determinants of healthy eating will need to take into account the dietary, linguistic, and cultural diversity found in contemporary Canadian society, along with temporal and spatial variation in food cost and availability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".