Alexithymia and its relationships with eating behavior, self esteem, and body esteem in college women.
Bibliographic record
Abstract
The aim of this study was to estimate prevalence rate of alexithymia and eating disorder (ED) as well as to explore the relationships between alexithymia and eating behavior, self esteem, and body esteem in non-clinical college women. A total of 313 Japanese college women were asked to make entries of age, height, and body weight, and to answer the full items in the Japanese version of the Toronto Alexithymia Scale (TAS-20), Eating Attitude Test (EAT-26), Rosenberg Self Esteem Scale (RSES), and Body Esteem Scale (BES). The frequency of alexithymics who scored 61 points or more of the TAS-20 was 28.7%, and the frequency of students with potential ED who scored 20 points or more of the EAT-26 was 8.7%. The prevalence of potential ED in the alexithymics (14.0%) was significantly higher than that in the non-alexithymics (6.5%). The mean values of the RSES and BES scores were significantly different between the alexithymic and non-alexithymic groups. The TAS-20 scores were unrelated to the age and body mass index, but were significantly correlated to the EAT-26 (total score (r = 0.12, p = 0.04), bulimia and food preoccupation (r = 0.14, p = 0.01)), the RSES (r = -0.44, p < 0.001), and BES (total score (r = -0.22, p < 0.001), appearance (r = -0.23, p < 0.001), and weight (r = -012, p = 0.04)). These results suggest that, in non-clinical college women, alexithymia is a common psychological characteristic that is strongly correlated with self esteem and body esteem and that may influence eating 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".