Dietary intake among adults in Trinidad and Tobago and development of a quantitative food frequency questionnaire to highlight nutritional needs for lifestyle interventions
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To create a food list and develop a draft quantitative food frequency questionnaire (QFFQ) for Trinidad and Tobago. METHODS AND PROCEDURES: A mixed sampling method was used to obtain a representative sample and trained interviewers administered 24-h dietary recalls. Portion sizes were assessed and the most frequently reported foods were tabulated. MAIN OUTCOMES AND RESULTS: Results are from 155 men and 169 women aged 21-64 years. The most frequently reported food items were: full-cream milk (64%), rice (61%), and sweetened fruit drinks (50%). Carbonated drinks were consumed by 28%. The most frequently consumed fruits were banana (23%) and citrus (22%); < 20% consumed a vegetable food item. The final QFFQ contains 146 items: 19 breads/cakes/cereals; seven rice/pastas/noodles; 12 dairy; 26 meats/poultry/fish/soy products; 15 fruits; 34 vegetables; six legumes; 11 other; 12 drinks; four alcoholic drinks. CONCLUSIONS: A list of commonly consumed foods in Trinidad and Tobago was obtained and a draft QFFQ was prepared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".