Development and Validation of a Food Frequency Questionnaire for Evaluating the Nutritional Status of Patients with Serious Mental Illnesses (DIETQ-SMI) in Bahrain
Bibliographic record
Abstract
General food frequency questionnaires (FFQs) have not been tailored for or validated in individuals with psychiatric disorders. Given the unique eating behaviors of patients with serious mental illnesses (SMIs), custom-made tools are needed. Therefore, we developed and validated an FFQ customized to individuals with SMIs. A total of 150 adults with SMIs (schizophrenia, bipolar disorder, or major depression) were recruited from Bahrain. The participants completed the 50-item Dietary Intake Evaluation Questionnaire for Serious Mental Illness (DIETQ-SMI) FFQ and a 3-day food record (FR). The validity of the FFQ was assessed by comparing nutrient intake to FR intake using correlation and reliability statistics. The DIETQ-SMI demonstrated a good ranking validity compared to the FR based on correlation coefficients (rho 0.33 to 0.92) for energy and macro- and micronutrients. The FFQ had high internal consistency (McDonald’s omega = 0.84; Cronbach’s alpha = 0.91) and test–retest reliability (ICC > 0.90). The FFQ tended to estimate higher absolute intakes than the FR but adequately ranked the intakes. The FFQ value was correlated with the FR for all the items (p < 0.001). The DIETQ-SMI is a valid and reliable FFQ for ranking dietary intake in individuals with an SMI. It can help assess nutritional status and, subsequently, guide interventions in high-risk psychiatric populations.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".