A Scoping Review of Mindfulness Instruments: Mapping Available Uni- and Multi-Dimensional Relationship-Specific Measures in Couple Contexts
Bibliographic record
Abstract
Mindfulness scientists have developed relationship-specific trait mindfulness measures to assess individuals’ inclination to be mindful in intimate partner interactions. However, there is a lack of consensus regarding a robust trait mindfulness measure in couples or romantic relationships. Therefore, this review sought to identify existing relationship-specific mindfulness measures, examine their psychometric strength and limitations, and provide relevant recommendations. Our rigorous search of articles published between 2013 and 2023 yielded ten relevant studies out of 2411. These ten studies included three uni- and four multi-dimensional measures. The unidimensional measures, such as the Relationship Mindfulness Measure (RMM) and Interpersonal Mindfulness Scale (IMS), were found to be mostly cited and validated. All four multi-dimensional measures and the Attentive Awareness in Relationships Scale (AAIRS) lack evidence of how closely scores on the measures correlate with behavior as measured. Despite good internal consistency, reliability, and content validity, all four multi-dimensional measures and RMM lack a comprehensive evaluation of measurement properties, including test-retest reliability, measurement error, hypothesis testing, and responsiveness. The findings of this review highlight the need for more empirical studies, particularly in collectivistic cultural settings, to confirm the validity and reliability of the selected instruments and to guide future research in this area.
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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 | 0.027 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.037 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".