Food Addiction Support: Website Content Analysis
Bibliographic record
Abstract
BACKGROUND: Food addiction has a long history; however, there has been a substantial increase in published literature and public media focus in the past decade. Food addiction has previously demonstrated an overlap with overweight and obesity, a risk for cardiovascular disease. This increased focus has led to the establishment of numerous support options for addictive eating behaviors, yet evidence-based support options are lacking. OBJECTIVE: This study aimed to evaluate the availability and content of support options, accessible online, for food addiction. METHODS: A standardized Web search was conducted using 4 search engines to identify current support availability for food addiction. Through use of a comprehensive data extraction sheet, 2 reviewers independently extracted data related to the program or intervention characteristics, and support fidelity including fundamentals, support modality, social support offered, program or intervention origins, member numbers, and program or intervention evaluation. RESULTS: Of the 800 records retrieved, 13 (1.6%, 13/800) websites met the inclusion criteria. All 13 websites reported originating in the United States, and 1 website reported member numbers. The use of credentialed health professionals was reported by only 3 websites, and 5 websites charged a fee-for-service. The use of the 12 steps or traditions was evident in 11 websites, and 9 websites described the use of food plans. In total, 6 websites stated obligatory peer support, and 11 websites featured spirituality as a main theme of delivery. Moreover, 12 websites described phone meetings as the main program delivery modality, with 7 websites stating face-to-face delivery and 4 opting for online meetings. Newsletters (n=5), closed social media groups (n=5), and retreat programs (n=5) were the most popular forms of social support. CONCLUSIONS: This is the first review to analyze online support options for food addiction. Very few online support options include health professionals, and a strengthening argument is forming for an increase in support options for food addiction. This review forms part of this argument by showing a lack of evidence-based options. By reviewing current support availability, it can provide a guide toward the future development of evidence-based support for food addiction.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.033 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".