Modeling the Effect of Approach Motives, Self-Compassion and Mindfulness on Sexual Intimacy of Married Nurses with the Mediating Role of Alexithymia
Bibliographic record
Abstract
Objective: The purpose of this study was to model the effect of approach motives, self-compassion and mindfulness on the sexual intimacy of married nurses with the mediating role of alexithymia. Methods and Materials: The statistical population included all married nurses in Ilam city in 2023, from which 240 nurses were selected as a sample using available sampling. To collect data, Sacrifice Motives Questionnaire (SNQ), Self-Compassion Scale (SCS), Freiburg Mindfulness Questionnaire (FMI-SF), Couples Sexual Intimacy Questionnaire (CSIQ) and Toronto Alexithymia Scale (TAS) were used. In the present study, the evaluation of the proposed model was done using structural equation modelling, and the bootstrap method (AMOS-24 software) was used to test indirect relationships. Findings: The fit indices of the proposed model have an acceptable fit with the data and the direct paths of approach motives, self-compassion and mindfulness to alexithymia and alexithymia to sexual intimacy were statistically significant. Also, the indirect paths of approach motives, self-compassion and mindfulness to sexual intimacy through alexithymia were statistically significant. Conclusion: Alexithymia has a mediating role between predictor variables (approach motives, self-compassion and mindfulness) and criterion (sexual intimacy). In other words, the motivations of approach, self-compassion and mindfulness can cause sexual intimacy in nurses through the reduction of alexithymia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".