Development and Psychometric Evaluation of the Multidimensional Assessment of Social Anxiety (MASA)
Bibliographic record
Abstract
OBJECTIVE: Hybrid models of psychopathology propose to combine the current categorical diagnostic system with shared symptom dimensions common across various disorders. Recently, the first empirically derived hybrid model of social anxiety was developed, including both a specific factor for the diagnostic category of social anxiety and 5 nonspecific factors for related symptoms. The present investigation developed a self-report measure-the Multidimensional Assessment of Social Anxiety (MASA)-based on this model to further the research on hybrid models. METHOD: This investigation included three studies across large undergraduate (N = 411; 52.3% male; mean [M] age = 19.6) and clinical (N = 684; 61.4% female; M age = 35.8) samples, involving the administration of the MASA item pool and existing measures of related constructs. RESULTS: Through a series of psychometric evaluations, the initial 466-item pool was reduced to a final 38-item measure that represented 6 distinct scales with adequate model fit, internal consistency, test-retest reliability, and convergent and discriminant validity. CONCLUSIONS: Together, these studies likely will lead to additional research and advances in the development of the hybrid models of the mood and anxiety disorders through the dissemination and administration of the MASA.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".