The Treatment Acceptability/Adherence Scale: Moving Beyond the Assessment of Treatment Effectiveness
Bibliographic record
Abstract
It is becoming more broadly recognized that beyond effectiveness, the acceptability of interventions for anxiety disorders is an important consideration for evidence-based practice. Although advances in treatments for anxious psychopathologies have demonstrated that cognitive-behavioural interventions are more desirable than other types of psychotherapy or pharmacotherapy, there continue to be problems with adherence and dropout. It has been suggested that low treatment acceptability may be partially responsible for high dropout rates. Although a number of preliminary investigations in this domain have been conducted, further progress is hampered by the absence of a single self-report measure that assesses both acceptability and anticipated adherence. Therefore, the current paper aimed to test the psychometric properties of the newly developed Treatment Acceptability/Adherence Scale (TAAS). In two studies of brief cognitive-behavioural interventions, the TAAS was administered immediately following the therapy session. In Study 1 (N = 120 non-clinical undergraduates), the therapy included two variants of an exposure-based intervention for contamination fear. In Study 2 (N = 27 individuals with obsessive-compulsive disorder), the therapy was a cognitively based intervention evaluating a novel treatment technique for checking compulsions. Measures of convergent and divergent validity were included. Results demonstrated that the TAAS exhibited sound psychometric properties across the two samples. It is hoped that this measure will help clinicians to predict and intervene when a treatment is not acceptable and/or when the client anticipates poor adherence to it. Furthermore, the TAAS may aid researchers in continuing to improve upon effective interventions for anxiety and related 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 | 0.032 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".