Adaptation and Validation of the Well-Being Related to Food Questionnaire (Well-BFQ©) for the French-Speaking General Adult Population of Québec, Canada
Bibliographic record
Abstract
Efforts to develop effective strategies that improve dietary intake are needed; however, this improvement in diet quality must not be at the expense of well-being. The Well-Being related to Food Questionnaire (Well-BFQ©) is a tool that has been developed in France to comprehensively measure food well-being. Even though the same language is spoken in France and in Québec, cultural and linguistic differences are present, which supports the importance of adapting and validating this tool before its use in the Québec population. This study aimed to adapt and validate the Well-BFQ© for the French-speaking general adult population of Québec, Canada. The Well-BFQ© underwent a full linguistic adaptation process, including an expert panel adaptation step, a pretest among 30 French-speaking adult (18–65 years) Quebecers, and a final proofreading. The questionnaire was thereafter administered to 203 French-speaking adult Quebecers (49.3% females, MAGE = 34.9, SD = 13.5; 88.2% Caucasians; 54.2% with a university degree). The exploratory factor analysis showed a two-factor structure: (1) food well-being related to physical and psychological health (27 items) and (2) food well-being related to symbolic/pleasure of food (32 items). Internal consistency was adequate, with a Cronbach’s α of 0.92 and 0.93, respectively, for the subscales, and 0.94 for the total scale. The total food well-being score, as well as the two subscale scores, were associated with psychological and eating-related variables in expected directions. Overall, the adapted version of the Well-BFQ© was found to be a valid instrument to measure food well-being in the French-speaking general adult population of Québec, Canada.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".