Identifying analogue samples of individuals with clinically significant social anxiety: Updating and combining cutoff scores on the Social Phobia Inventory and Sheehan Disability Scale.
Bibliographic record
Abstract
The use of analogue samples, as opposed to clinical groups, is common in mental health research, including research on social anxiety disorder (SAD). Recent observational and statistical evidence has raised doubts about the validity of current methods for establishing analogue samples of individuals with clinically significant social anxiety. Here, we used data from large community samples of clinical and nonclinical participants to determine new cutoff scores on self-report measures of social anxiety symptoms and symptom-related impairment. We then examined whether using these newly determined cutoff scores alone or in combination improves the identification of individuals who have SAD from those who do not, revealing the most ideal cutoff combination to be 34 or above on the Social Phobia Inventory and 11 or above on the Sheehan Disability Scale. Finally, we compared the effects of our new cutoff scores with old cutoff scores by extracting analogue samples of participants with high social anxiety from historical data on seven large groups of undergraduate Psychology research participants from the authors' institution spanning the past 5 years (2018-2023). We observed that the new combined cutoff scores identified markedly fewer students as having high social anxiety, lending credibility to their utility. We also observed a striking increase in levels of social anxiety symptoms in the undergraduate population from before to after the COVID-19 pandemic. Of note, most participants were under 30 and identified as Caucasian or Asian women, indicating that future research is needed to examine whether our findings generalize to diverse populations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".