Association of depression and resilience with fertility quality of life among patients presenting to the infertility centre for treatment in Karachi, Pakistan
Bibliographic record
Abstract
BACKGROUND: In Pakistan there is a dire need to explore the quality of life in infertile males and females and its undesirable psychological outcomes. This, study aimed to compare the quality of life (QoL) of males and females visiting an infertility centre for treatment and to assess its association with resilience, depression, and other socio-demographic factors. METHODS: An Analytical Cross-Sectional study was conducted amongst infertile males and females at the Australian Concept Infertility Medical Centre (ACIMC), Karachi, Pakistan. The non-probability (purposive) sampling strategy was used to recruit the participants. The sample size was 668. Data was analysed using STATA version 12. FertiQoL tool, Beck II Depression Inventory Tool and Resilience Scale 14 (RS-14) were used for assessing the quality of life, depression and resilience respectively of infertile patients. RESULTS: Total 668 infertile patients, 334 males and 334 females participated in the study. The mean age was 35.53 ± 6.72, among males, and 30.87 ± 6.12 among females. The mean resilience scores were significantly higher among males, (77.64 ± 8.56), as compared to females (76.19 ± 8.69) (95% CI; - 2.757, - 0.1347). However, a significantly higher proportion of females were depressed (13.8%) as compared to males (6%). The mean QoL scores for the general health domain, emotional domain, mind and body domain, and relational domain, and the total QoL were significantly higher in males as compared to females (p value< 0.001); however, QoL for the social domain was not significantly different in both the groups. On multivariable linear regression resilience and depression among males had a significant association with QoL, after adjusting for the covariates educational status, monthly income, and number of friends. Similar association was observed among females after adjusting for the covariate monthly income only. CONCLUSION: Fertility related QoL of men and women has a significant association with no formal education, number of friends, income, depression and resilience. Therefore, health care professionals in the field of infertility must be adequately trained to respond to the needs of individuals going through these psychological problems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".