A Comparison of Methods for Teaching Discrimination of Acceptance and Commitment Therapy/Training Processes in Samples of Verbal Behaviour
Bibliographic record
Abstract
Accurate identification of the six core processes of Acceptance and Commitment Therapy/ Training (ACT) is foundational for practitioners, yet no published research has compared methods for teaching this skill. This exploratory study compared the efficacy and efficiency of two methods (discrimination training, DT and self-paced, mastery-based training, SPMB) for training graduate students to identify ACT processes using speech samples. Two ACT processes were selected as training targets based on a logical analysis conducted with six behaviour analysts trained in ACT. Respondents rated fusion/defusion and lack of present moment awareness (PMA)/PMA to be of relatively equal discrimination difficulty. The training procedures were compared in an adapted alternating treatment design embedded within a delayed concurrent multiple baseline design across three students. In the DT condition modules, participants viewed training videos, completed exercises, whereas in the SPMB condition modules, participants read chapters of an ACT text in the SPMB condition modules. Following modules in either condition, participants completed skill assessments to assess their ability to discriminate the target ACT skills. Participants listened to samples of verbal behaviour then selected which ACT process they observed. Skill assessment results suggest that the most efficacious training method is SPMB. However, when false positives (identifying a sample of verbal behaviour as the target skill when it is the control skill) are included in scoring, DT is the more efficacious training method. Participants did not rate one training method more favorably than the other. Findings have the potential to inform future ACT research and ultimately increase the effectiveness of ACT interventions.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".