Dealing with Anxiety: Relationships among Interpersonal Attachment Style, Psychological Wellbeing and Trait Anxiety
Bibliographic record
Abstract
Anxiety is a major contributor to poor quality mental health for many people in our community, and is a leading cause of presentations at medical and health clinics. Patterns of trait anxiety, or dysfunctional responding, have become ingrained in individuals’ approaches to problems they face. Research has shown that psychological wellbeing and interpersonal attachment style are both predictors of trait anxiety. However, the relationships among these variables have not been clarified. The current study sought to determine whether psychological wellbeing mediates the relationship between interpersonal attachment style and trait anxiety, and which of the six psychological wellbeing subscales would contribute most to any mediation effects. A convenience sample of 149 adult participants from South East Queensland, Australia completed a series of online questionnaires including a demographic questionnaire, the Trait Anxiety subscale of the State-Trait Anxiety Inventory (STAI-Form Y2), the Inventory of Parent and Peer Attachment (IPPA), Ryff’s Psychological Wellbeing Scale (PWB), and a Social Desirability Scale (SDS-17). Psychological Wellbeing was found to partially mediate the relationship between interpersonal attachment style and trait anxiety. The Positive Relations with Others subscale of the PWB was the only significant sub-scale of the PWB that significantly predicted trait anxiety. Overcoming anxiety appears to be most related in our sample to those who deal better with interpersonal relations. Targeting this aspect in treatment approaches appears most likely to lead to improved outcomes for clients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".