Psychological distress as predictor of quality of life in men experiencing infertility: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Infertility is associated with impairment in human life. The quality of life (QOL) construct allows measuring the impact of health conditions in a broader way. The study aimed to explore the impact of the psychological distress on QOL's dimensions in men experiencing infertility. METHODS: 162 men were completed a socio-demographic form, SF-36, WHOQOL-BREF, Beck Anxiety Inventory and Beck Depression Inventory. Hierarchical regressions included demographic and clinic variables, and subsequently depression and anxiety were added. RESULTS AND DISCUSSION: Model 1 was not accurate in predicting QOL. R2 values ranged from 0.029 (Social Functioning) to 0.149 (Mental Health). Eight domains were not associated with any of the predictors. In the second model, a R2increase was observed in all domains. R2 of QOL scores ranged from .209 (Role Physical) to .406 (Social Functioning). The intensity of the depression was a significant predictor for all outcomes. The load of depression was higher than the ones of the socio-demographic and clinical variables. Anxiety levels have also presented the same effect, but with less intensity. CONCLUSION: Subthreshold depression and anxiety were major predictors of QOL in men experiencing infertility. Health professionals need to include assessment of psychological symptomatology to plan more efficient interventions to infertile patients.
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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 | 0.003 |
| 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 | 0.001 |
| 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".