Measurement, prevalence, and psychological risk factors associated with addictive food consumption: Development of a new food addiction scale and evidence from a national largescale sample
Bibliographic record
Abstract
BACKGROUND AND AIMS: To date, a number of studies have investigated the prevalence and correlates of addictive food consumption. However, these studies have mostly relied on models that comprised a narrow range of variables in often small and heterogenous samples. The purpose of the present study was to comprehensively examine the measurement aspects, the prevalence, and the psychological correlates of addictive eating among a largescale national sample of Turkish adults. METHOD: Participants (N = 24,380, 50% men, Mage = 31.79 years, age range = 18-81 years) completed a battery of tests including the Food Addiction Risk Questionnaire (FARQ), the Brief Symptom Inventory, the Toronto Alexithymia Scale, the Positive and Negative Affect Schedule, and the Experiences in Close Relationships-Revised. RESULTS: According to analyses conducted, the FARQ had a uni-dimensional factor structure. Based on Item Response Theory (IRT) calculated cut-off scores, 2.3% of the participants were at risk of addictive eating patterns, whilst criteria varied in their discriminating ability. The correlates of addictive food consumption were being male, being younger, having lower education, presenting with higher alcohol use, psychiatric symptoms, alexithymia, positive/negative affect, and anxious attachment. CONCLUSION: These results suggest that a minority of Turkish community are at risk for addictive food consumption and that adverse psychological states promote this problematic behavior.
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 | 0.004 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".