Comparison between SCOFF and EAT-26: an Italian Study on Young Female Adults
Bibliographic record
Abstract
Early screening for eating disorders often occurs in primary care or in non-psychiatric settings by using rapid and easy questionnaires, such as EAT-26 and the SCOFF. Here, the study compares the Italian translations of SCOFF and EAT-26 to confirm their screening validity and risk distribution in a non-clinical sample composed by young women (aged 18-30 years). Our findings show a strong risk correlation between the two tools, with frequent and overlapped questions exploring similar constructs. However, mismatching results regarding the detection of clinical risk for eating disorders involve about a quarter of the sample, mostly due to their internal specificity/sensitivity, translation discrepancies, tool training and use modality. In general, both screening tools are reliable for EDs detection in the general population. In the case of EAT-26, the combined use of both questionnaires improve robustly the risk detection (+23%) for eating disorders in young female adults, especially in universities and work places adopting an online administration. Further studies may occur to better understand the specific factors influencing mismatching results, in terms of EDs risk, between the two questionnaires.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".