Augmenting Behavioural Activation Treatment with the Behavioural Activation and Inhibition Scales
Bibliographic record
Abstract
BACKGROUND: Although behavioural activation therapy is effective for depression there is always room for improvement, and also the need to extend this treatment modality to anxiety disorders. METHOD: A search was conducted for an easy to apply and effective method of achieving these aims. RESULTS: To both enhance the effectiveness of behavioural activation treatment for depression and facilitate its extension to anxiety disorders, it is proposed that the Behavioral Approach/Activation System (BAS) and Behavioral Inhibition System (BIS) be incorporated. BIS/BAS Scales are easy to administer and evaluate ensuring that there is minimal added complexity. Overall, BAS, BAS subscale (Drive, Reward Responsiveness, and Fun Seeking) and BIS scores provide valuable information pertaining to a person's approach and avoidance responses. This general information, plus that derived from specific scale items, can be used to guide more focused and effective behavioural activation strategies. Brief case examples are provided to demonstrate how the BIS/BAS Scales can be applied. CONCLUSION: BIS/BAS Scales offer an easy to apply and effective means of enhancing behavioural activation therapy for depression and extending this treatment modality to anxiety disorders.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".