Acceptance and Commitment Therapy for Behavior Analysts
Bibliographic record
Abstract
This book provides a thorough discussion of acceptance and commitment therapy or training (ACT) and a guide for its use by behavior analysts. The book emphasizes how the intentional development of six core behavioral processes – values, committed action, acceptance, defusion, self-as-context, and present moment awareness – help establish the psychological flexibility needed to acquire and maintain adaptive behaviors that compete with maladaptive behavior patterns in verbally able clients. Split into three parts, the book discusses the history and controversy surrounding the rise of acceptance and commitment strategies in behavior analysis and shows how the processes underlying ACT are linked to foundational behavioral scientific principles as amplified by stimulus equivalence and relational learning principles such as those addressed by relational frame theory. In a careful step-by-step way, it describes the best practices for administering the acceptance and commitment procedures at the level of the individual client, organizational systems, and with families. Attention is also given to the ethical and scope-of-practice considerations for behavior analysts, along with recommendations for conducting on-going research on this new frontier for behavior analytic treatment across a myriad of populations and behaviors. Written by leading experts in the field, the book argues that practice must proceed from the basic tenants of behavior analysis, and that now is the opportune moment to bring ACT methods to behavior analysts to maximize the scope and depth of behavioral treatments for all people. Acceptance and Commitment Therapy for Behavior Analysts will be an essential read for students of behavior analysis and behavior therapy, as well as for individuals on graduate training programs that prepare behavior analysts and professionals that are likely to use ACT in their clinical practice and research.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".