Perceptions of Canada's food guide to health eating among urban Ontario adults
Bibliographic record
Abstract
This thesis is an investigation of perceptions of ' Canada's Food Guide to Healthy Eating' among urban Ontario adults. Framed within the risk literature, the objectives of this research were to develop a conceptual analysis of the meaning of population-based dietary advice and to understand responses to this form of risk communication. Working within a constructivist paradigm, the constant comparative method was used to analyze 40 open-ended, in-depth interviews. Participants were purposively recruited to vary in gender (20 men, 20 women), age (24--71), and education (some secondary to completion of graduate degree). The interviews revealed a widespread perception of risk, yet not all participants believed that following the 'Food Guide' would reduce the risk of chronic disease. Four main themes that emerged from the data (relevance, trust, skepticism, and resentment) distinguished the participants into a continuum of responses to the 'Food Guide' (accept, negotiate, ignore, reject, resist). Across this continuum, from those who accepted the 'Food Guide' to those who resisted it, the personal relevance and level of trust in this advice diminished considerably. In contrast, levels of skepticism and resentment increased across the response categories. Participants who accepted the 'Food Guide' welcomed it as relevant and trustworthy advice. Those who displayed a negotiated response also trusted it, but most searched elsewhere for more relevant information. Others in this category could not comply because of financial constraints. Although participants who ignored the 'Food Guide' believed it was trustworthy, it had little relevance in their lives. Participants who rejected the 'Food Guide' considered it personally irrelevant (but it was suitable for 'others'). Perceiving that marketing boards and the food industry had influenced the content, they were skeptical of the recommendations. Participants who resisted the 'Food Guide's' advice believed that following it would bring harm, not health. Including the public in discussions about the legitimacy of stakeholders involved in the 'Food Guide's' development can create more effective policy; however, the wealth of information currently available overwhelms the 'Food Guide' as a source of dietary advice.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".