Introspective Awareness and Its Predictive Power on Health Anxiety: A Cross-sectional Study
Bibliographic record
Abstract
Background: The COVID-19 pandemic has precipitated widespread health anxiety, affecting populations globally.This study aimed to investigate the relationship between introspective awareness and health anxiety levels.Methods: Employing a cross-sectional design and the convenience sampling method, 350 residents of Richmond Hill, Canada, with an age of 18 and above participated in this study in 2023.Health anxiety was assessed using the health anxiety inventory (HAI), while introspective awareness was measured via the multidimensional assessment of interoceptive awareness (MAIA), encompassing eight subscales.Pearson correlation coefficient and linear regression analysis were used to explore the predictive relationships between introspective awareness components and health anxiety.Results: The participants exhibited a slight female predominance (53.43%), diverse age distribution, and the majority had post-secondary education (81.43%).Noticing (r=-0.45,P<0.001) and emotional awareness (r=-0.48,P<0.001) demonstrated significant negative correlations with health anxiety, indicating their potential protective roles, while other subscales showed no significant predictive role individually (P>0.05).The regression model revealed that these components significantly predicted health anxiety levels, accounting for approximately 42% of the variance (R 2 =0.42,F (2, 347) =48.35,P<0.001).Specifically, increases in noticing and emotional awareness were associated with decreases in health anxiety scores (B=-3.45 and B=-4.12, respectively; P<0.001).Conclusion: Enhancing aspects of introspective awareness, such as noticing and emotional awareness, could be crucial in developing interventions aimed at reducing health anxiety in pandemic conditions and beyond.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".