ArticleHealth psychology and behavioral medicine2026
Test anxiety predictors inventory (tapi): development and initial validation of a predictor-oriented instrument for medical students.
Article in Health psychology and behavioral medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Test anxiety (TA) is common among medical students and may adversely affect mental well-being and academic performance. However, existing instruments such as the Test Anxiety Inventory (TAI) predominantly quantify the severity and manifestations of anxiety (outcomes) rather than identifying modifiable, intervention-relevant antecedents (predictors). This study aimed to develop and validate the Test Anxiety Predictors Inventory (TAPI) to support concurrent identification of at-risk students and to inform domain-specific, targeted interventions in resource-constrained medical training settings. Methods: A cross-sectional census survey was conducted at Can Tho University of Medicine and Pharmacy (Dec 2024-Mar 2025; 382/389 valid responses, 98.2%). A 30-item pool was piloted in 71 students, then refined through iterative EFA (3 rounds; ML extraction, Direct Oblimin) and CFA (ML and DWLS estimators). Internal consistency, convergent and discriminant validity, correlation with TAI, and concurrent classification performance (multivariable logistic regression; ROC-AUC) were assessed. Because census sampling yielded one dataset, EFA/CFA reflect internal testing pending external validation; 'prediction' denotes concurrent classification, not temporal forecasting. Results: TA prevalence (TAI ≥ 48) was 67.0% (95% CI 62.15-71.54). The final 14-item TAPI loaded on three factors-EAS (7), AMF (4), SRP (3)-explaining 77.99% of variance (KMO = 0.935). The correlated three-factor CFA showed good fit (CFI = 0.957; RMSEA = 0.083). The second-order DWLS model showed excellent fit but produced a Heywood warning with negative latent variance for EAS; the correlated three-factor model is therefore the more defensible representation. Reliability was high (α/ω = 0.89-0.95; CR = 0.92-0.96; AVE = 0.74-0.82; HTMT < 0.85). TAPI-TAI correlation was r = 0.576; classification model AUC = 0.804, sensitivity = 0.938, specificity = 0.452. Conclusions: TAPI shows promising internal structure and concurrent classification performance. High sensitivity supports first-stage screening, while modest specificity indicates it should complement, not replace, formal diagnostic procedures.
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