Alexithymia, infertility‐related stress and quality of life in women undergoing an assisted reproductive treatment
Bibliographic record
Abstract
Abstract The investigation of the association between alexithymia and quality of life in infertility is a relatively neglected area of research. The aim of this study was to evaluate the association between alexithymia and infertility‐related quality of life in women during Assisted Reproductive Treatment. Data were collected in a clinic in Rome, 93 infertile women completed the 20‐item Toronto Alexithymia Scale (TAS‐20), the Fertility Quality of Life (FertiQoL) questionnaire and a socio‐demographic questionnaire. TAS‐20 total and two factors—Difficulty in Identifying Feelings (DIF) and Difficulty in Describing Feelings (DDF)—showed significant negative correlations with the overall questionnaire and with both Core and Treatment modules of FertiQoL. The regression model explained the 43% variance in FertiQol overall scores (R2 = 0.43; adjusted R2 = .38); a significant effect was reported for the number of previous attempts (beta = 0.20; p < .04), TAS‐20 DIF (beta = −0.47; p < .001) and TAS‐20 Externally Orientated Thinking (EOT) (beta = 0.20; p < .04); after applying Benjamini‐Hochberg correction procedure only TAS‐20 DIF maintained its significance. Alexithymia is associated with a worsened quality of life in infertile women; specifically, low difficulties in identifying feelings were associated to higher quality of life. Further investigations are needed also to develop specific therapeutic interventions aimed to promote emotional abilities in infertile people.
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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 | 0.000 | 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) | 0.002 | 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".