Using the Food Skills Questionnaire (FSQ) to Evaluate a Cooking Intervention for University Students: A Pilot Study
Bibliographic record
Abstract
Purpose: To pilot test the Food Skills Questionnaire (FSQ) to evaluate a cooking intervention. Methods: Students attending Western University were invited to participate in 3 cooking classes over a 3-month period. All participants were asked to complete the FSQ pre- and post-intervention. The FSQ evaluated food skills in 3 domains—Food Selection and Planning, Food Preparation, and Food Safety and Storage—with a maximum score of 100 per domain. Domain scores were then computed as a weighted average for the Total Food Skills Score out of 100. Open-ended questions assessed participants’ perceptions of the classes. Results: Forty-four students participated. There was a significant increase in food planning (70.6 ± 13.5–77.6 ± 14.3, P < 0.01), food preparation (67.5 ± 14.0–74.9 ± 12.9, P < 0.01), food safety (78.0 ± 9.9–80.8 ± 13.0, P = 0.04), and total food skills (71.9 ± 8.9–77.8 ± 10.6, P < 0.01) post-intervention. Content analysis of open-ended questions indicated that participants enjoyed healthy recipes, supportive Peer Educators, discussions, the cooking experience, socializing, and the safe environment. Conclusions: The FSQ shows strong potential for evaluating basic (e.g., peeling, chopping, slicing) to intermediate (e.g., meal planning) food skills in an effective and feasible manner. It can also capture changes in specific domains, allowing the development of more focused nutrition education and skills-based interventions.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".