Prospects for a clinical science of mindfulness-based intervention.
Bibliographic record
Abstract
Mindfulness-based interventions (MBIs) are at a pivotal point in their future development. Spurred on by an ever-increasing number of studies and breadth of clinical application, the value of such approaches may appear self-evident. We contend, however, that the public health impact of MBIs can be enhanced significantly by situating this work in a broader framework of clinical psychological science. Utilizing the National Institutes of Health stage model (Onken, Carroll, Shoham, Cuthbert, & Riddle, 2014), we map the evidence base for mindfulness-based cognitive therapy and mindfulness-based stress reduction as exemplars of MBIs. From this perspective, we suggest that important gaps in the current evidence base become apparent and, furthermore, that generating more of the same types of studies without addressing such gaps will limit the relevance and reach of these interventions. We offer a set of 7 recommendations that promote an integrated approach to core research questions, enhanced methodological quality of individual studies, and increased logical links among stages of clinical translation in order to increase the potential of MBIs to impact positively the mental health needs of individuals and communities.
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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 | 0.063 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".