Using latent profile analysis to uncover the combined role of anxiety sensitivity and test anxiety in students’ state anxiety
Bibliographic record
Abstract
Background: 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. Aims: 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. Sample and methods: = 2.1) from 13 public and private schools completed self-reported measures of state/trait anxiety, anxiety sensitivity, and test anxiety. Results: 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. Conclusion: 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.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".