A Correlational Analysis of the Relationships among Intolerance of Uncertainty, Anxiety Sensitivity, Subjective Sleep Quality, and Insomnia Symptoms
Bibliographic record
Abstract
In this study, we used structural equation modeling to investigate the interplay among Intolerance of Uncertainty (IU), Anxiety Sensitivity (AS), and sleep problems. Three hundred undergraduate students completed the Intolerance of Uncertainty Scale, the Intolerance of Uncertainty Inventory, the Anxiety Sensitivity Index, the Beck Depression Inventory, the State-Trait Anxiety Inventory, the Pittsburgh Sleep Quality Index and the Insomnia Severity Index. 68% and 40% of the students reported poor sleep quality or sub-threshold insomnia problems, respectively. Depression and anxiety levels were above the cut-off for about one-fourth of the participants. Structural equation modeling revealed that IU was strongly associated with AS, in turn influencing both insomnia severity and sleep quality via depression and anxiety. Significant indirect effects revealed that an anxious pathway was more strongly associated with insomnia severity, while a depression pathway was more relevant for worsening the quality of sleep. We discussed the results in the frameworks of cognitive models of insomnia. Viewing AS and IU as antecedents of sleep problems and assigning to AS a pivotal role, our study suggested indications for clinical interventions on a population at risk for sleep disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".