Investigating quality of life and health-related quality of life in infertility: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To review the findings on quality of life (QOL) and health-related quality of life (HRQOL) among infertile women, men and couples. DESIGN: Systematic review. METHODS: Publications between January 1980 and July 2009 in Medline, PsycInfo, Embase and Health and Psychosocial Instruments were compiled using the following inclusion criteria: papers published in peer-reviewed journals; written in English, French, Spanish or Portuguese; presented original findings; assessed quality of life or health-related quality of life as an outcome; included infertile subjects without other clinical conditions; used validated measures. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Quality of life and health-related quality of life. RESULTS: Fourteen studies were included in the review. Educational level, will to have children, poor marital relationship, previous in-vitro fertilisation attempt and duration of the infertility were predictors of lower mental health scores in infertile men. Women had significant lower scores on mental health, social functioning and emotional behaviour. Among infertile subjects, women had lower scores in several QOL or HRQOL domains in comparison to men. CONCLUSIONS: Evidences indicate important QOL or HRQOL impairments in infertile women. Among men, it does not appear to be intense. There is scarce knowledge regarding the impact of infertility on couples.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".