Fear of anxiety or fear of emotions? Anxiety sensitivity is indirectly related to anxiety and depressive symptoms via emotion regulation
Bibliographic record
Abstract
Background and objectives: Both anxiety sensitivity (AS) and maladaptive emotion regulation (ER) may contribute to anxious and depressive symptoms. Given the overlap between ER and AS—They both pertain to maladaptive beliefs about emotions (BE)—We tested whether AS would demonstrate an indirect relationship with anxiety and depressive symptoms via BE and ER. Design: Participants were 150 undergraduate students who completed an online survey. Methodology: Participants completed the Anxiety Sensitivity Index-3, difficulties with emotion regulation scale, Beliefs about Emotions Questionnaire, and Depression Anxiety Stress scales. Results: Bootstrapped serial mediation analyses demonstrated that the relationship between AS and anxiety symptoms was partially attributable to BE and ER, but not to BE alone. Similarly, the relationship between AS and depressive symptoms was completely attributable to BE and ER, but not to BE alone. Supplemental analyses suggested that beliefs about the controllability of emotions/anxiety were particularly important in the indirect nature of the relationship between AS and anxiety and depressive symptoms. Conclusions: AS and ER play an important role in the maintenance of anxiety and depressive symptoms. These results highlight the uncontrollability of emotions as a potentially important construct in cognitive-behavioural models of anxiety and emotion regulation. The cross-sectional design and non-clinical sample limit the generalizability of our findings; replication and extension in other samples and via experimental designs is warranted.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".