Bibliographic record
Abstract
Abstract Mindfulness practice and protocols—often referred to as mindfulness-based interventions (MBIs)—have become increasingly popular in every sector of society, including healthcare, education, business, and government. Due to this exponential growth, thoughtful reflection is needed to understand the implications of, and interactions between, the historical context of mindfulness (insights and traditions that have been cultivated over the past 25 centuries) and its recent history (the adaptation and applications within healthcare, therapeutic and modern culture, primarily since the 1980s). Research has shown that MBIs have significant health benefits including decreased stress, insomnia, anxiety, and panic, along with enhancing personal well-being, perceptual sensitivity, processing speed, empathy, concentration, reaction time, motor skills, and cognitive performance including short- and long-term memory recall and academic performance. As with any adaptation, skillful decisions have to be made about what is included and excluded. Concerns and critiques have been raised by clinicians, researchers, and Buddhist scholars about the potential impact that the decontextualization of mindfulness from its original roots may have on the efficacy, content, focus, and delivery of MBIs. By honoring and reflecting on the insights, intentions, and work from both historical and contemporary perspectives of mindfulness, the field can support the continued development of effective, applicable, and accessible interventions and programs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".