Gender-related Differences in Correlations among BMI, Salivary Testosterone and Cortisol and Depression and Alexithymia Scores in University Students
Bibliographic record
Abstract
Introduction: Women have high anxiety and depression incidence compared to men. In the present study, gender-related differences in correlations among BMI, salivary testosterone and cortisol and depression and alexithymia scores in university students. Methods: A total of 88 Nigerian university students were involved in the study. Participants were 20 men and 68 women who were 17-25 years of age. Salivary assay of cortisol and testosterone were done using Enzyme-linked Immunosorbent Assay Kits. The Self-Reporting Questionnaire (SRQ) 20 adapted from WHO was used to screen for depression. Toronto Alexithymia Scale was used to assess the points associated with alexithymia. Results: In the present study, there was a significant negative correlation between testosterone and depression in only men, but not in the total sample and women. There were significant positive correlations between depression and alexithymia scores in the total sample and women, but not in men. Discussion: The gender difference in the relation of salivary testosterone with depression showed again that gender is a very important factor in behavioral studies including depression. It can be stated that testosterone can be an important hormonal factor to prevent or decrease depression or depressive thoughts in men but not in women. The positive correlations between depression and alexithymia scores suggest that high depression in female university students is related to social and environmental factors, but not low testosterone. Conclusion: These results suggest that high depression in female healthy university students is may be due to social, cultural, and ecological factors, but not hormonal (cortisol and testosterone) factors.
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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 | 0.000 | 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) | 0.003 | 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".