Assisted Reproductive Treatment: the role of Alexithymia, Romantic Attachment, Marital Relationship on Couple’s Quality of Life
Bibliographic record
Abstract
Introduction: Fertility problems can cause serious implications for couples’ wellbeing. Aims of the study were to evaluate: possible differences between women and men in quality of life, alexithymia, romantic attachment and quality of marital relationship during an Assisted Reproductive Treatment (ART); predictive effects of these variables on quality of life of women and men. Methods: 84 women (age m=38.1 sd=5.5) and 25 men (m=37.5 sd=5.6) participated in the study. At the beginning of ART women and men completed: socio-demographic questionnaire, Fertility Quality of Life (FertiQuoL), 20-item Toronto Alexithymia Scale (TAS-20), Experience in Close Relationship-Revised (ECR-R) and Couple Relation Inventory (CRI). Results: Differences in FertiQuoL Total (p=.02) Emotional (p=.001), Mind-Body (p=.05) and Tolerability (p=.001) subscales scores emerged in the direction of a lower quality of life in women. Furthermore, women scored significantly higher in TAS-20 Difficulty in Identifying Feelings (DIF;p=.04) and CRI Idealization (p=.01), whereas men scored significantly higher in CRI Erotic Fantasy (p=.04). Several significant associations among quality of life, alexithymia, quality of marital relationship and attachment dimensions were reported in women and men. Regression analysis showed the predictive effect of DIF (p=001) and CRI Attunement with partner (p=.01) on women’s quality of life. Conclusions: The findings support a more severe effect of infertility and its treatments on women than men. The findings highlight the predictive effect of the difficulty in identifying feeling and attunement with the partner on the quality of life of women during the ART. Clinical implication will be discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".