Data_Sheet_1_Using latent profile analysis to uncover the combined role of anxiety sensitivity and test anxiety in students’ state anxiety.PDF
Bibliographic record
Abstract
Background<p>Studies report a growing tendency for students to experience state anxiety in schools. However, the combination of individual susceptibilities likely to trigger students’ anxious states remains unclear.</p>Aims<p>This study examined whether distinct profiles of students emerge regarding their susceptibility to anxiety sensitivity and/or test anxiety and evaluated whether students’ profile predicted anxious states. We also verified whether susceptibility profiles varied across gender, school level, and school type.</p>Sample and methods<p>In total, 1,404 Canadian students in Grades 5 and 10 (589 boys; M<sub>age</sub> = 15.2, SD = 2.1) from 13 public and private schools completed self-reported measures of state/trait anxiety, anxiety sensitivity, and test anxiety.</p>Results<p>Latent profile analyses identified four susceptibility profiles: (1) Double-susceptibility: highest anxiety sensitivity and test anxiety scores; (2) Unique-susceptibility to test anxiety: high test anxiety score and low anxiety sensitivity score; (3) Unique-susceptibility to anxiety sensitivity: high anxiety sensitivity score and low test anxiety score; and (4) No-susceptibility: lowest anxiety sensitivity and test anxiety scores. The profiles comprised 12, 9, 6, and 73% of the sample, respectively, and their membership varied across gender and school type, but not across school levels. A linear mixed-effect model showed that state anxiety varied significantly between profiles, where the Double-susceptibility profile predicted the highest state anxiety scores, followed by the two Unique-susceptibility profiles (indifferently), and the No-susceptibility profile.</p>Conclusion<p>Beyond their theoretical contribution to the state–trait anxiety literature, these findings suggest that selective interventions designed more specifically for students with the Double-susceptibility profile may be worthwhile. Results also highlight the high proportion of students with the No-susceptibility profile and shed light on the reassuring portrait regarding students’ anxiety.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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 teacher head, 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".