Principles for a Responsible Integration of Mindfulness in Individual Therapy
Bibliographic record
Abstract
Abstract Objectives Mindfulness-based interventions (MBIs) like mindfulness-based stress reduction (MBSR) and mindfulness-based cognitive therapy (MBCT) teach mindfulness in a group-based format. Empirical research has shown that many therapists working in individual therapy integrate mindfulness practices (e.g., body scan, sitting meditation) into their treatments. However, research on this topic is in its infancy. The purpose of this paper is to present recommendations for a responsible use of mindfulness in individual therapy. Methods Informed by a literature review, an expert group developed guidelines for a responsible use of mindfulness in individual therapy. Results Recommendations for the following issues were developed: (a) different types of integration; (b) diagnoses/clinical problems for which integration of mindfulness in individual therapy could be useful; (c) qualification of therapists; (d) case formulation; (e) the inquiry process; (f) types and optimal duration of mindfulness practices in individual therapy; (g) managing difficult experiences; (h) integration of mindfulness into individual therapy training programs. Finally, we formulate important topics for research on the integration of mindfulness into individual therapy. Conclusions By formulating recommendations for the most important issues of the integration of mindfulness into individual therapy, we want to stimulate the discussion on a responsible use of mindfulness in this setting. Since research on this topic is scarce, our recommendations can only be tentative.
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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 | 0.072 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".