The mediating role of alexithymia in prediction of nutritional attitudes based on difficulties in emotion regulation
Bibliographic record
Abstract
Background: Unhealthy nutritional attitudes and emotion regulation difficulties are among the most important problems among young people, but the key question is whether natural cognition plays a mediating role in emotion regulation difficulties and nutritional attitudes? Aims: The aim of this study was to investigate the mediating role of natural misery in predicting nutritional attitudes based on emotion regulation difficulties. Method: It was a descriptive-correlational one and was performed on 130 students of Payam Noor Najafabad University who were selected by available sampling method. Research questionnaires included Toronto Natural Intelligence Scale (1994), Garner & Garfinkel (1979) Nutrition Attitude Scale, and Gratz & Roemer (2004) Emotion Regulation Scale. Data were analyzed using structural equation method. Results: The results showed that direct path coefficients between predictor (emotion regulation difficulties) and criterion variable (nutritional attitudes) were not significant (p>0/05), but indirect path coefficients between These variables were significant with mediation of natural odor (p<0/05). Conclusions: Natural curiosity mediates the prediction of nutritional attitudes based on the difficulty of emotion regulation, which demonstrates the role of natural curiosity in emotion regulation and improvement of nutritional attitudes.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".