The Dialectical Behavior Therapy Adherence Coding Scale (DBT ACS): Psychometric properties.
Bibliographic record
Abstract
The Dialectical Behavior Therapy Adherence Coding Scale (DBT ACS) is an observer-rated measure used to evaluate the extent to which therapists deliver individual and group DBT with adherence to the manual. Despite its frequent use in clinical trials of DBT, relatively little is known about its psychometric properties. The present study utilized data from six clinical trials conducted in research and community settings with a variety of patient populations. Across these studies, the DBT ACS was used to code a total of 1,271 DBT individual therapy sessions and 180 DBT group sessions. Results indicate the DBT ACS computed global score has good internal consistency (α = .81) and excellent interrater reliability (ICC = .93). A confirmatory factor analysis found that a single factor yielded acceptable goodness of fit indices. The DBT ACS discriminated between DBT and another treatment and between research and community therapists. Across studies, variability in adherence scores was attributable more to therapists (33%) than to patients (15%). Both therapist and patient variability were higher in effectiveness than efficacy trials. Generalizability coefficients indicated that 5 sessions are needed to estimate a dependable adherence score at the patient level, whereas 9-15 sessions are needed to achieve adequate generalizability at the therapist level. Fewer sessions were needed to yield dependable scores for community therapists compared to research therapists. The DBT ACS appears to be a reliable, valid, and dependable method of assessing therapist adherence to individual and group DBT across diverse treatment settings, therapist types, and patient populations. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".