A Brief, Digital Music-Based Mindfulness Intervention for Black Americans With Elevated Race-Based Anxiety and Little-to-No Meditation Experience (“healing attempt"): Replication and Extension Study
Bibliographic record
Abstract
BACKGROUND: Race-based anxiety is a critical health issue within the Black community. Mindfulness interventions hold promise for treating race-based anxiety in Black Americans; however, there are many barriers that prevent Black Americans from using these treatments, such as low cultural relevance, significant time burdens, and excessive costs. OBJECTIVE: This study is a replication and extension of findings that "healing attempt"-a brief (<60-minute), digital, music-based mindfulness intervention-is a feasible and acceptable intervention for race-based anxiety in Black Americans. In this study, we tested this research question among those with little-to-no meditation experience. METHODS: The participants were 4 Black American adults with elevated race-based trait anxiety and little-to-no meditation experience. We used a series of multiple-baseline single-case experiments and conducted study visits on Zoom (Zoom Video Communications) to assess whether the intervention can decrease state anxiety and increase mindfulness and self-compassion in Black Americans. We also assessed feasibility and acceptability using quantitative and qualitative scales. RESULTS: In line with our hypotheses, "healing attempt" increased mindfulness/self-compassion (Tau-U range: 0.57-0.86; P<.001) and decreased state anxiety (Tau-U range: -0.93 to -0.66; P<.001), with high feasibility and acceptability (the average likelihood of recommending "healing attempt" was 88 out of 100). CONCLUSIONS: "healing attempt" may represent a feasible intervention for race-based anxiety in Black Americans with elevated race-based anxiety and little or no mindfulness experience. Future between-subjects randomized feasibility trials can assess whether the intervention can give rise to lasting improvements in race-based anxiety, mindfulness, and self-compassion. TRIAL REGISTRATION: OSF Registries osf.io/k5m93; https://osf.io/k5m93.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".