Bibliographic record
Abstract
BACKGROUND: Gender differences in anxiety disorders and personality have been reported, but few studies have evaluated gender differences in the relationship between anxiety and personality in a normative sample. AIM: The goal of the present study was to evaluate gender differences anxiety and personality in a normative sample. METHOD: In the present study, 124 (94 female, 30 male) undergraduate students were evaluated with the State Trait Anxiety Inventory and the NEO Five Factor Inventory of personality. RESULTS: The female participants showed greater Trait Anxiety and Neuroticism than male participants, with trends for the female participants to show greater State Anxiety and Agreeableness than the male participants. Both male and female participants showed strong relationships between State and Trait Anxiety and increased Neuroticism. Additionally, both male and female participants showed a relationship between decreased Extraversion and increased Anxiety. Neither gender showed any relationship between anxiety and Openness, consistent with other studies. The female participants, but not the male participants, showed strong relationships between higher State and Trait Anxiety and lower Agreeableness and lower Conscientiousness. CONCLUSION: The different relationship of anxiety and personality in the male and female participants may suggest that this relationship may have a different underlying structure in each gender.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".