Assessment of key food skills among Canadians; results from the Canadian Community Health Survey rapid response modules on food skills (LB380)
Bibliographic record
Abstract
Food skills contribute to healthier eating; yet, some studies suggest that these skills are declining. This project aims to strengthen our understanding of food skills in Canada and to provide baseline data for monitoring population trends. Two Canadian Community Health Survey modules on food skills were conducted in Nov‐Dec 2012 (n=9559) and Jan‐Feb 2013 (n=10156) among a representative sample of Canadians aged 12 years and older. Questions focused on measuring cooking ability, the transference of skills to children, meal planning, shopping practices and mechanical cooking skills. With regards to cooking skills, most Canadians reported that they can prepare most meals (39%) or cook most dishes with a recipe (24%). Approximately 15% of Canadians do not participate in meal preparation. Most adults living with children share a family meal, sitting at the table together either every day (48%) or almost every day (25%). Further, children make suggestions for family meals (67%), help prepare meals (60%) and participate in shopping for groceries (68%) in most households with children. When shopping for groceries, most Canadians plan their meals (62%) and use a grocery list (74%) and nutrition labels (63%). While most Canadians reported being very good at peeling, choping or slicing (63%) and cooking raw meat/chicken/fish (61%), many reported basic to no skills in canning (63%) and freezing (31%). These findings will help inform the development of intervention and promotion programs focused on improving foods skills in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".