Predictors of response to an attention modification program in generalized social phobia.
Bibliographic record
Abstract
OBJECTIVE: At least 3 randomized, placebo-controlled, double-blind studies have supported the efficacy of computerized attention modification programs (AMPs) in reducing symptoms of anxiety in patients diagnosed with an anxiety disorder. In this study we examined patient characteristics that predicted response to AMP in a large sample of individuals diagnosed with generalized social phobia. METHOD: The sample comprised 112 individuals seeking treatment for generalized social phobia who completed a randomized clinical trial comparing AMP (n = 55) with a placebo condition (i.e., attention control condition; n = 57). We examined the following domains of baseline predictors of treatment response: (a) demographic characteristics (gender, age, ethnicity, years of education); (b) clinical characteristics (Axis I comorbidity, trait anxiety, depression); and (c) cognitive disturbance factors (attentional bias for social threat, social interpretation bias). RESULTS: Results revealed that ethnicity predicted treatment response across both conditions: Participants who self-identified as non-Caucasian displayed better overall response than did Caucasians. The only prescriptive variable to emerge was attentional bias for social threat at preassessment. Participants in the AMP group who exhibited larger attentional bias scores displayed significantly greater reductions in clinician-rated social anxiety symptoms than did their counterparts in the attention control condition. CONCLUSIONS: These results suggest that AMP may be targeted to individuals most likely to benefit from these programs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".