Full-Time Employment, Diet Quality, and Food Skills of Canadian Parents
Bibliographic record
Abstract
Purpose: To explore the associations between full-time employment status, food skills, and diet quality of Canadian parents. Methods: A sample of Canadian parents (n = 767) were invited to complete a web-based survey that included sociodemographic variables, questions about food skills, and a validated food frequency questionnaire. Results were analyzed with linear and logistic regression models, controlling for sociodemographic variables and multiple testing. Results: After controlling for covariates and multiple testing, there were no statistically significant differences in foods skills between parents’ employment status. Time was the most reported barrier for meal preparation, regardless of work status, but was significantly greater for full-time compared with other employment status (P < 0.0001). Additionally, parents who worked full-time had lower odds of reporting food preferences or dietary restrictions (P = 0.0001) and health issues or allergies (P = 0.0003) as barriers to food preparation, compared with parents with other employment status. These results remained statistically significant even after controlling for covariates and multiple testing. Conclusions: Overall, food skills did not differ significantly between parents’ employment status. Time, however, was an important barrier for most parents, especially those working full time. To promote home-based food preparation among parents, strategies to manage time scarcity are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".