School food offer at lunchtime: assessing the validity and reliability of a web-based questionnaire
Bibliographic record
Abstract
OBJECTIVES: To develop and validate a web-based self-diagnostic questionnaire on school food service offer aimed at food service managers (FSM) by: (i) identifying relevant indicators of school food offer, developing a questionnaire and validating the concept using an expert panel; (ii) validating the questions by comparing the FSM's responses with observations by dietitians and (iii) undergoing a qualitative evaluation of the tool through direct observation and short interviews. DESIGN: Mixed methods. SETTING: Quebec, Canada. PARTICIPANTS: Nine experts validated the theoretical constructs and indicators on which the questionnaire was based. Inter-rater reliability tests were conducted with thirty-nine FSM, who then participated in interviews about platform functionality satisfaction. Twenty school stakeholders participated in the survey pertaining to their use of the personalised report. RESULTS: The questionnaire focused on the main school food service's lunchtime offer and comprised twenty-six questions. The overall strength of agreement was good, and all questions' strengths of agreement were fair to excellent except for one question. Qualitative data reached saturation and showed that navigation through the questionnaire was fluid. Improvements were suggested to increase user-friendliness and simplicity of both the platform and questionnaire. Results from the survey showed that all respondents were either satisfied or very satisfied with their personalised report. CONCLUSIONS: We successfully developed and validated a web-based self-diagnostic questionnaire. The final version facilitates knowledge mobilisation with school stakeholders and offers a new opportunity for the assessment and surveillance of school food offer.
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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 | 0.048 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".