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Record W2146882335 · doi:10.1017/s1352465811000415

Augmenting Behavioural Activation Treatment with the Behavioural Activation and Inhibition Scales

2011· article· en· W2146882335 on OpenAlexaff
Brad Bowins

Bibliographic record

VenueBehavioural and Cognitive Psychotherapy · 2011
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsBehavioral activationAnxietyPsychologyModality (human–computer interaction)PsychotherapistDepression (economics)Clinical psychologyCognitive psychologyPsychiatryCognitionComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.101
GPT teacher head0.349
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2011
Admission routes1
Has abstractyes

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