Comparing the Effectiveness of Expressive Writing and Relaxation on Mental Health and Treatment Success in Women Undergoing Assisted Reproductive Technology: A Randomized Controlled Trial
Bibliographic record
Abstract
Objective: Infertility and its treatments can lead to mood disorders such as depression, anxiety, stress. This study aimed to compare the effects of relaxation and expressive writing on depression, anxiety, stress, and treatment success in women utilizing assisted reproductive technology methods. Materials and methods: In this parallel randomized clinical controlled trial, 90 infertile women undergoing assisted reproductive technology were involved. Participants were randomly assigned to three groups: writing (n=30), relaxation (n=30), and control (n=30). The writing and relaxation groups received either expressive writing or relaxation interventions, while the control group only received routine treatment. The primary outcomes measured in the study were depression, anxiety, and stress, with treatment success as the secondary outcome. All participants completed the Depression, Anxiety, and Stress Scale at the beginning of the treatment cycle and again before ovarian puncture. Statistical analysis was performed using the Chi-squared, Kruskal-Wallis, and Wilcoxon tests. A p-value<0.05 was considered statistically significant. Results: The results showed that mean differences in depression (P=0.001) and stress scores (P=0.011) before and after intervention in the writing group were significantly higher than in the control group. Additionally, only the writing group experienced a significant decrease in depression (P=0.016). However, there was no significant difference in other measured outcomes among the three groups. Conclusion: It is recommended to conduct more well-designed studies to further investigate the effects of expressive writing and relaxation techniques.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".