Factors That Predict Food Skills in Canadian Gym Members: A National Cross-Sectional Survey
Bibliographic record
Abstract
This study determined predictors of food skills in Canadian gym members. A random sample of gym members were invited to complete a validated Food Skills Questionnaire with supplementary questions. All questions/variables significantly associated (p < 0.05) and fair-to-moderately correlated (r ≥ 0.40) with Total Food Skills (TFSs) were analyzed by multiple regression. The respondents’ (n = 576) mean ± SD age was 41.3 ± 14.8 years, with 67.3% females and 13.2% students. The mean TFSs score was 77.1 ± 11.9 (maximum 100). Females reported higher TFSs than males; however, this did not remain significant when nutrition-related beliefs were considered. Increasing age, taking a nutrition/cooking course, teen meal preparation, primary cook, time preparing weekend meals, believing that preparing healthy food is important, and self-reported nutritional quality of diet and nutrition knowledge were positively associated with TFSs (p < 0.05). Purchasing food/beverages from convenience stores, buying pre-prepared dinners, and being a student were negatively associated with TFSs (p < 0.05). The strongest predictors of TFSs were self-reported nutrition knowledge and nutritional quality of diet. The adjusted R2 increased by 0.30 when food-related experiences/behaviours and nutrition-related beliefs were included in the final model, which accounted for 50% of the variance in TFSs. Food experiences/behaviours and nutrition beliefs, which are associated with food skills, are potential intermediary targets for programs and/or research to improve food skills.
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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 | 0.001 | 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 | 0.000 |
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".