Acceptance and Commitment Therapy and Psychological Well-Being: A Narrative Review
Bibliographic record
Abstract
Acceptance and commitment therapy (ACT) is an innovative psychological intervention emphasizing psychological flexibility and values-driven actions to enhance overall well-being. Unlike traditional cognitive-behavioral therapies (CBT), which often focus on altering maladaptive thoughts and behaviors, ACT encourages the acceptance of negative thoughts and emotions while fostering a commitment to personal values. This review explores ACT's effectiveness in promoting psychological well-being (PWB) by encompassing emotional, psychological, and social dimensions. Key aspects of PWB include emotional regulation, life satisfaction, and the reduction of experiential avoidance. The theoretical foundations of ACT are based on relational frame theory (RFT), which addresses cognitive fusion and experiential avoidance as central contributors to psychological distress. Extensive research, including meta-analyses and randomized controlled trials (RCTs), supports ACT's efficacy in treating various psychological issues and improving PWB. Despite strong empirical support, several gaps remain, including the need for more longitudinal studies, the exploration of cultural adaptability, and research on specific populations. Recommendations for future research include examining the long-term effects of ACT, its application across diverse cultural contexts, and its efficacy among underrepresented groups. This review underscores ACT's potential as a versatile therapeutic approach, providing valuable insights for mental health professionals and researchers.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 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".