Measuring Mindfulness in Black Americans: A Psychometric Validation of the Five Facet Mindfulness Questionnaire
Bibliographic record
Abstract
Abstract Objectives: Black Americans disproportionately experience higher levels of chronic stress. Mindfulness is a promising, cost-efficient treatment option for reducing stress and related mental health outcomes such as depression and anxiety. The Five Facet Mindfulness Questionnaire (FFMQ) is one of the most widely used tools to measure mindfulness; however, Black American samples have been underrepresented in validation studies of the FFMQ. Consequently, the validity of the FFMQ within Black Americans is unknown. The present study assessed the psychometric properties and nomological network of the original 39-item FFMQ (FFMQ-39) and the short form 15-item FFMQ (FFMQ-15) among a non-clinical, Black American sample in the United States. Methods: In a longitudinal study, 586 Black Americans completed either the FFMQ-39 or the FFMQ-15 at two time points one month apart. Results: Exploratory and confirmatory factor analyses supported a five-factor structure in both questionnaires. Both questionnaires had good fit indices ( RMSEA > .05, SRMR > .05, CFI > .92, TFI > .92) and demonstrated strong test-retest reliability, expected associations with nomological network variables, and invariance across gender, mindfulness meditation experience, depression level, everyday discrimination, lifetime discrimination, household income, ethnic heritage, and skin tone. Conclusion: The results indicate that both the FFMQ-39 and the FFMQ-15 can validly and reliably measure mindfulness in a non-clinical, Black American sample. These findings contribute to cultural generalizability and mindfulness assessment within underrepresented populations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".