A Critical Review of Laboratory-Based Studies Examining the Relationships of Social Anxiety and Alcohol Intake
Bibliographic record
Abstract
Research has revealed inconsistencies regarding the relationship between social anxiety and alcohol use. The goal of the current review is to examine lab-based studies that have been conducted in an attempt to help disentangle the social anxiety - alcohol link. Specifically, this review focuses on the most prominent theories present in this area of research, namely, the Tension Reduction Theory, the Stress-Response Dampening Model, the Self-Awareness Model, the Attention Allocation Model, and the Appraisal-Disruption Model. The review then describes the empirical studies that have been conducted to test predictions derived from each of these theories. This is followed by a discussion of some methodological considerations in this area of research, including an examination of participant characteristics, study selection criteria, alcohol administration procedures, the nature of the anxiety-inducing tasks that have been used in this area of research, and the different types of outcome measures that are typically used to measure social anxiety. The review ends with some tentative conclusions and directions for future research, including recommendations to recruit individuals with high levels of trait social anxiety, to closely monitor blood alcohol levels achieved at different time points during the study, to examine more interaction-based social anxiety provoking tasks, and to employ a wider range of outcome measures (e.g., cognitive and behavioural outcomes relevant to social 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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".