Dispositional mindfulness profiles and psychological symptoms: a latent profile analysis
Bibliographic record
Abstract
Objectives: In this study, we aimed to (1) examine profiles of mindfulness using the short form of the FFMQ (FFMQ-SF), (2) identify the demographic predictor (i.e., sex) of mindfulness profile membership, and (3) examine associations of mindfulness profiles with psychological outcomes (i.e., anxiety and depressive symptoms). Methods: This cross-sectional study included 604 individuals recruited from Amazon's Mechanical Turk platform (MTurk). We performed latent profile analyses (LPA) to explore the individual profiles based on scores on dispositional mindfulness facets. Dispositional mindfulness was measured using the Five-Facet Mindfulness Questionnaire-Short Form (FFMQ-SF). Depression was assessed using the Patient Health Questionnaire-9 (PHQ-9). Anxiety symptoms were measured by the Generalized Anxiety Disorder Scale (GAD-7). Results: We identified three mindfulness profiles including, Judgmentally Describing, Low Mindfulness, and Non-Judgmentally Describing. Participants in the Low Mindfulness group were more likely to be women compared to the other two profiles (Judgmentally Describing and Non-Judgmentally Describing groups). Participants in the Low Mindfulness group had the highest levels of anxiety and depressive symptoms, whereas individuals in the Non-Judgementally Describing group had the lowest levels of depression and anxiety. Conclusion: In the present study, we demonstrated three profiles of the FFMQ-SF, which had differential relationships with anxiety and depressive symptoms. Consideration of such profiles may help clinicians to develop more fine-tuned mindfulness-based psychological interventions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".