The Diet and Behaviour Scale (DABS): Testing a New Measure of Food and Drink Consumption in a Cohort of Secondary School Children From the South West of England
Bibliographic record
Abstract
A multitude of instruments exist to assess dietary intake. Many, however, are time-consuming to administer, focus primarily on macronutrient composition or the effects of specific micronutrients, and do not consider the effects of foods and drinks that fail to add significant nutritional contributions (e.g. energy drinks, chewing gum). In order to address these issues the current paper introduces the Diet and Behaviour Scale (DABS). This 29-item questionnaire is used to measure both the frequency and amount of consumption of common foods and drinks, with a particular onus on functional foods and dietary variables of current concern. The DABS was administered to a large cohort of secondary school children from the South West of England at two time-points. At Time 1 (December, 2012) the cohort consisted of 3071 pupils, 2030 of whom responded to the questionnaire; at Time 2 (June, 2013) 3323 pupils made up the cohort, and 2307 completed the questionnaire. Factor analysis yielded a four-factor solution labelled Junk Food, Caffeinated Soft Drinks/Gum, Healthy Foods, and Hot Caffeinated Beverages. When investigating how these factors were related to demographic and lifestyle variables, Chi-square analyses uncovered the following relationships: being male was associated with high Junk Food intake; sleeping for fewer hours than average, achieving low school attendance, and having poor general health were associated with high intake of Caffeinated Soft Drinks/Gum; lower school year, more sleep, more frequent exercise, and good general health were associated with high intake of Healthy Foods; and being male, having a special educational needs status, reporting fewer hours of sleep, and being in an older school year were associated with a high intake of Hot Caffeinated Beverages. Whilst controlling for demographic and lifestyle variables, logistic regression analyses determined that poor general health was predicted by high consumption of Caffeinated Soft Drinks/Gum and low consumption of Healthy Foods. Though additional studies are required to further test the questionnaire and its associated factor structure, the DABS is considered to be a useful self-report measure of certain aspects of dietary intake, and is proposed as a useful tool for future research investigating dietary influences on psychological variables such as mental wellbeing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".