Alexithymia and body image disturbances in women with Premenstrual Dysphoric Disorder
Bibliographic record
Abstract
BACKGROUND: To evaluate alexithymia and body image in women with Premenstrual Dysphoric Disorder and test whether alexithymic traits influence severity of Premenstrual Dysphoric Disorder or body distress. METHODS: Sixty-four consecutive women with a DSM-IV diagnosis of Premenstrual Dysphoric Disorder and age range of 18-45 were recruited. Alexithymia was measured with the Italian version 20-items Toronto Alexithymia Scale. Severity of premenstrual mood symptoms was measured through the use of Visual Analogue Scales. Body concerns were assessed with the Body Uneasiness Test, Body Shape Questionnaire and Body Attitude Test. Additional measures were Rosenberg Self-Esteem Scale and Sheehan Disability Scale. RESULTS: Prevalence of alexithymia in our sample was 31.3% (n=20). Alexithymics showed higher scores on all rating scales (p range 0.001-< 0.001). Difficulty in Identifying Feelings and Difficulty in Describing Feelings subscales of Toronto Alexithymia Scale were predictors of severity of Premenstrual Dysphoric Disorder in the multiple linear regression analysis. CONCLUSIONS: Alexithymia was associated with more severe Premenstrual Dysphoric Disorder. Alexithymic women with Premenstrual Dysphoric Disorder exhibited significantly poorer appearance evaluation and body satisfaction than non-alexithymic women.
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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 | 0.000 | 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) | 0.002 | 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".