Meditation Breath Attention Scores (MBAS): Development and investigation of an internet-based assessment of focused attention during meditation practice.
Bibliographic record
Abstract
Meditation Breath Attention Scores (MBAS) represent a self-report, state measure of focused attention (FA) during the practice of meditation. The MBAS assessment procedure involves sounding a bell at periodic intervals during meditation practice, at which times participants indicate if they were attending toward breathing (scored 1) or if instead they had become distracted (e.g., by mind wandering; scored 0); scores are then tallied to yield participants' MBAS for that meditation. The current study developed and evaluated a fully automated and Internet-based version of MBAS in 1,101 volunteers. Results suggested that: (a) MBAS are internally consistent across bell rings; (b) MBAS total scores exhibit a non-normal distribution identifying subgroups of participants with particularly poor or robust FA during meditation; (c) MBAS decrease linearly with the duration of meditation practices, indicating that participants tend to experience less FA later as opposed to earlier in the meditation; (d) in the case of eyes-open meditation, MBAS are higher when the amount of time between bells is shorter; (e) MBAS correlate with various self-reported subjective experiences occurring during meditation; and (f) MBAS are weakly associated with higher trait mindful "acting with awareness," lesser ADHD-related symptoms of inattentiveness, and estimated minutes of meditation practiced in the past month. In sum, results provide further support for the construct validity of MBAS and serve to further characterize the dynamics of individual differences in FA during meditation. (PsycINFO Database Record
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".