Alexithymia and fertility-related stress
Bibliographic record
Abstract
The investigation of the relationship between alexithymia, the inability to identify and describe feelings and the absence of fantasies, and fertility-related distress is a relatively neglected area of research. The aims of this study were to examine: (1) the prevalence of alexithymia in a sample of infertile women, and (2) the association between alexithymia, coping strategies, and fertility-related stress. This study included 160 infertile women undergoing in vitro fertilization in a public fertility clinic from September of 2013 to December of 2013. Self-report instruments were used to measure alexithymia (Toronto Alexithymia Scale-20), coping (COPE), and fertility-related stress (Fertility Problem Inventory). Bivariate and multiple linear regression were used. A high alexithymia score was positively associated with age, infertility duration, and low educational and economic level. Multivariate analyses showed that, controlling for demographic factors, high avoidance coping, low problem-appraisal coping, and high alexithymia were positively associated with fertility-related stress (β = 0.309, p < .001, β = -0.203, p = .006, β = 0.151, p = .050, respectively). Results of this study indicated that alexithymia during fertility treatment was associated with maladaptive coping strategies and psychological stress. In addition, the association between alexithymia and duration of infertility may be interpreted as secondary alexithymia acts as a coping strategy in infertile 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".