Alexithymia and Body Image in Saudi Arabian Females (University Students): Preliminary Data
Bibliographic record
Abstract
Introduction: Alexithymia has been the focus of much recent research in relation to emotional regulation and eating problems amongst psychological disorders. It is dealing with difficulties in processing, expressing, and awareness of emotion. Body image has also been researched, especially amongst women, and its relation emotion. Objective: The aim of this exploratory study is to examine the presence of alexithymia and how this is related to body image amongst a group of female Saudi University students. Methods: 152 Arabic female students from a local University participated in the study. The Toronto Alexithymia Scale-20 (TAS-20 Arabic), the Therapeutic Alexithymia scale (PTA Scale) short scale, body image scale, and figure body image scale were all used in the study. Results: The results showed that there is significant correlation between TAS and body image scale. Further, results showed that body image scale is best predictor of alexithymia in regression analysis. Discussion: Alexithymia has not been studied in university students in KSA. We also explored its relationship to body image and found there is a significant correlation. Alexithymia is present and needs much research in this sample and beyond, in both clinical and non-clinical groups. Conclusion: This is the first study in an Arabic population to show the alexithymia is prevalent amongst this sample and it is significantly related to poor body image. Further studies are suggested to explore further psychological variables related to body image and eating problems, as well as on clinical samples is indicated.
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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 | 0.001 |
| 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) | 0.003 | 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".