A Digital Music-Based Mindfulness Intervention for Black Americans With Elevated Race-Based Anxiety: A Multiple-Baseline Pilot Study
Bibliographic record
Abstract
BACKGROUND: Race-based anxiety is a substantial health issue for the Black community. Although mindfulness interventions have demonstrated efficacy for alleviating anxiety, three central barriers prevent Black Americans from accessing existing mindfulness treatments: high costs, excessive time commitments, and limited cultural relevance. There is a need for novel mindfulness interventions for the Black community that can overcome these barriers. OBJECTIVE: The goal of this web-based study was to examine the preliminary efficacy, feasibility, and acceptability of a novel digital music-based mindfulness intervention for middle-to-low-income Black Americans with elevated race-based anxiety. METHODS: This study used a nonconcurrent multiple-baseline design (n=5). The intervention featured contributions from Lama Rod Owens (a world-renowned meditation teacher and LA Times best-selling author) and Terry Edmonds (the former chief speechwriter for President Bill Clinton). We examined the effect of the intervention on state anxiety and assessed its feasibility and acceptability using quantitative and qualitative measures. RESULTS: Results revealed that administration of the intervention led to significant decreases in state anxiety (Tau-U range -0.75 to -0.38; P values<.001). Virtually all feasibility and acceptability metrics were high (ie, the average likelihood of recommending the intervention was 98 out of 100). CONCLUSIONS: This study offers preliminary evidence that a digital music-based mindfulness intervention can decrease race-based anxiety in Black Americans. Future research is needed to replicate these results, test whether the intervention can elicit lasting changes in anxiety, assess mechanisms of change, and explore the efficacy of the intervention in real-world contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".