The role of partner support in infertility-related quality of life in couples seeking fertility treatment
Bibliographic record
Abstract
Infertility is a common issue, with significant impacts on couples’ lives. Infertility and its treatments can place considerable stress on both partners and lead to relationship insecurity. Several researchers have shown that infertility can reduce the quality of life of both members of the couple. Since partners represent the main source of support for each other in the context of infertility, examining partner support as a potential protective factor for these couples seems highly justified. The objective of this study was to examine the association between partner support and infertility-related quality of life assessed 3 months later among 83 couples using medically assisted reproduction. Partners individually completed online questionnaires at baseline and 3 months later. Path analyses using the actor-partner interdependence model revealed that a person’s perception of greater emotional, informational, and tangible partner support was associated with their own higher emotional and relational infertility-related quality of life 3 months later. Women’s perception of greater physical support was also associated with their partner’s higher relational quality of life. The findings suggest that interventions targeting partner support could reduce the negative effects of infertility and its treatments on infertile couples’ quality of life.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".