Regulation of Emotional, Marital Satisfaction and Marital Lifestyle of Fertile and Infertile
Bibliographic record
Abstract
The purpose of this present study is investigating of relationship and compare between emotion regulation, marital satisfaction and lifestyle of the fertile and infertile couples in Isfahan. The sample consisted of two groups of 200 people in fertile and infertile couples in Isfahan who were selected based on availability. To collect information was used from questionnaires lifestyle, marital satisfaction and cognitive emotion regulation. The results showed that lifestyle and marital satisfaction in fertile and infertile no significant difference in cognitive emotion regulation fertile and infertile couples, but there is a significant difference. Infertile couples have lower cognitive emotion regulation. As well as lifestyle and marital satisfaction in fertile and infertile couples have a significant positive relationship that the relationship is stronger for infertile couples. Lifestyle and cognitive emotion regulation, marital significant negative relationship with cognitive emotion regulation has a significant positive relationship between fertile and infertile couples 17.7 % of fertile couple’s marital satisfaction and cognitive emotion regulation with fertile couples to explain them is not a predictor of marital satisfaction. But lifestyle 22.6 % of infertile couples infertile couples marital satisfaction and cognitive emotion regulation in the first and 7.4% of infertile couples to predict marital satisfaction, Therefore, it is essential that interventions for infertile couples to be cognitive of the training set to handle the excitement of infertility and prevent problems related to it.
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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".