Cooking, shopping, budgeting and tasting: Insights from a simple foods and intuitive eating workshop series in Toronto
Bibliographic record
Abstract
Studies show that people who are in a precarious financial position are more susceptible to poor health and face greater challenges in accessing expert health advice than financially-secure individuals (Vozoris & Tarasuk, 2003). Furthermore, research also indicates that the overall quality of diet decreases as its proportion of ultra-processed food increases (Moubarac et al, 2012). Alongside a registered dietitian, this study involved the development and implementation of a free, 12-part, weekly workshop series for parents and caregivers who struggle with food finances, which explored cooking and otherwise preparing minimally-processed foods along with notions of eating intuitively. Using a qualitative research approach, the author sought to understand how such a curriculum may impact participants’ food understandings and senses of health-related empowerment, and also to explore what barriers may function to impede eating intuitively and simply for Canadians whose food budgets are regularly challenging. Five themes emerged from analysis of the data: Culinary skill development and support, budgeting and access, habits, body literacy, and managing complexity. Findings from this study can be seen to support the promotion of a combination of simple food preparation techniques, intuitive eating strategies, and critical media awareness as fruitful elements of accessible and equitable health promotion.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".