Development of the Canadian food intake screener for adolescents based on Canada’s Food Guide 2019 healthy eating recommendations
Bibliographic record
Abstract
Abstract Background Assessing adolescents’ dietary intakes in relation to Canada’s Food Guide 2019 (CFG-2019) recommendations on healthy food choices is a critical component to public health surveillance efforts. The study aimed to develop a brief self-administered screener to assess food intake based on CFG-2019 food choices recommendations among English- and French-speaking adolescents aged 10–17 years living in Canada. Methods The development and assessment of the content validity of the tool was undertaken in collaboration with Health Canada advisors and informed by external content experts, including nutrition researchers and practitioners. Following a rapid review of screeners used among children aged 6–17 years, an initial draft was developed, and content validity was assessed by an expert panel with expertise in public health nutrition and questionnaire validation (English n = 13, French n = 7). Two rounds of cognitive interviews were then conducted with adolescents (English n = 15, French n = 14) to assess comprehension and further refine the screener items. Cognitive testing using a direct probing approach was conducted iteratively in two phases to assess understanding of questions and incorporate feedback from adolescents to improve the clarity and wording of the items at each phase. Results Following the expert panel and iterative discussions with Health Canada advisors, four items were removed from the initial 14-item screener as these items were deemed not sufficiently reflective of the CFG recommendations and one item asking about water intake was tested. Cognitive testing revealed that the items were well understood overall, and feedback at each interview round enabled additional refinements to improve comprehension. The resulting screener includes 10 items designed to rapidly assess food intake based on CFG-2019 recommendations on healthy food choices for adolescents aged 10 to 17 years. Conclusions The Canadian Food Intake Screener for Adolescents/Questionnaire court canadien sur les apports alimentaires des adolescents is designed to rapidly assess dietary intake over the past week among children aged 10 to 17 years. Before it can be used for research and population-level nutrition surveillance, further research is needed to develop a scoring system and evaluate the screener’s construct validity and reliability.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".