Latent classes of eating disorders and addictions by sex: Implication of alexithymia and stressful life events in youths
Bibliographic record
Abstract
Introduction: Eating disorders (EDs) and behavioural addictions show common psychological vulnerability factors such as alexithymia and stressful life events (SLE). This study aims, firstly, to explore the prevalence and latent profiles of participants based on their risk of suffering EDs, gambling disorder (GD), alcohol and/or drug abuse, and compulsive buying (CB) by sex. Secondly, it aimed to test whether alexithymia and having experienced SLE are associated with group membership. Methods: The sample was predominantly drawn from university students and social networks. It was composed of 352 young adults between 18 and 35 years old, of whom 77.8% were women and 22.2% men. Results: The results showed that the most prevalent disorders of the sample were alcohol, EDs, CB, drugs and GD, respectively. Moreover, latent class analyses were conducted based on the risk of suffering EDs or addictions by sex. Three main profiles were found: 'Men with addictions', 'Healthy women' and 'Women with EDs'. Finally, differences in SLE and alexithymia levels were tested by latent classes. "Men with addictions" and "Women with EDs" had higher scores on alexithymia and SLE than the group of "Healthy women". However, the group of "Women with EDs" (class 3) reported significantly higher levels of SLE and alexithymia than the other two groups. Discussion and conclusion: In conclusion, we discuss the possibility that some vulnerability factors operate generally and transdiagnostically in EDs and addictive disorders. The identification of clinical phenotypes could complement and deepen prediction, prevention and treatment research in clinical settings. The need to take sex and gender differences into account is reinforced.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".