Cluster analysis of anxiety sensitivity among adults who smoke
Bibliographic record
Abstract
Anxiety sensitivity, the fear of anxiety-related sensations due to beliefs that they will cause elicit harmful consequences, has emerged as an important factor in understanding the complex relations between cigarette smoking and the co-occurrence of mental/physical health concerns. Anxiety sensitivity research has focused on the global construct, with less attention given to individual lower-order dimensions. Yet, individuals vary in the degree to which they experience fears about physical, mental, and social concerns. This study examined whether individuals who smoke cluster into distinct groups based on their levels of anxiety sensitivity and, if so, their relations to mental health symptoms and smoking processes. Participants included 570 adults who smoked cigarettes daily. Results indicated that there were three homogeneous clusters ("Very High," "Moderate," and "Low") that were consistent across all anxiety sensitivity dimensions. There were group differences between the clusters, with the "Very High" group showing elevated negative emotional symptoms relative to the other two clusters. There were also differences for various smoking processes. The findings suggest that a more comprehensive approach to modeling anxiety sensitivity can be achieved using cluster analysis and that distinct groups evince theoretically aligned and clinically significant relations to mental health and smoking processes..
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".