Arabic Translation, Psychometric Evaluation, Measurement Invariance, and Network Analysis of the Sexual Five-Facet Mindfulness Scale (FFMQ-S) Among Married Arab Women
Bibliographic record
Abstract
This study aimed to develop and validate the Sexual Five-Facet Mindfulness Scale (FFMQ-S), a 19-item measure assessing sexual mindfulness in a sample of Arab married women. Initial items were translated and culturally adapted through a bilingual committee approach. The FFMQ-S was tested using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), measurement invariance testing, and exploratory graph analysis (EGA) with a sample of 720 married Arab women. A five-factor model was found to best fit the data, with all items retained. The FFMQ-S demonstrated excellent internal consistency (Cronbach's α > .85) and evidence of convergent and discriminant validity, with higher sexual mindfulness associated with lower sexual distress and greater sexual satisfaction. Measurement invariance confirmed the stability of the factor structure across different marriage durations. Network analysis further supported the interrelations among the five facets. Findings suggest that sexual mindfulness, as a multi-dimensional construct, is linked to positive sexual outcomes. The FFMQ-S can serve as a valuable tool for assessing sexual mindfulness and guiding clinical interventions aimed at improving sexual well-being in women.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".