Mindfulness meditation and improvement of anxiety among women trying to conceive
Bibliographic record
Abstract
Background and objective: Anxiety disorders are prevalent among women attempting to conceive, contributing to emotional distress and potentially affecting fertility outcomes. Traditional pharmacological treatments pose concerns during preconception and pregnancy, emphasizing the need for effective non-pharmacological interventions. Mindfulness meditation has emerged as a promising approach for reducing anxiety, yet limited research focuses on its impact on women trying to conceive. This quality improvement project aimed to evaluate the effectiveness of daily mindfulness meditation in reducing anxiety levels among women actively attempting to conceive within one year.Methods: A nine-week cross-sectional survey study was conducted with 14 women aged 32-42 receiving care at a private practice in Chicago, Illinois. Participants engaged in daily ten-minute guided mindfulness meditation sessions using the Insight Timer app. Anxiety levels were assessed using the Generalized Anxiety Disorder 7-item (GAD-7) scale at baseline, weeks 3, 6, and 9. Data analysis included one-way ANOVA to compare mean GAD-7 scores across time points.Results: GAD-7 scores demonstrated a clinically meaningful decrease over time, with mean scores declining from 9.6 at baseline to 4.2 at week 9. Although the reduction was not statistically significant (p = .083), sustained improvement in anxiety levels suggests the intervention's potential benefit. The highest drop in anxiety occurred between weeks 3 and 6, with effects persisting post-intervention.Conclusions: Daily mindfulness meditation may serve as a valuable, non-pharmacological strategy for reducing anxiety in women attempting to conceive. Despite the small sample size and lack of statistical significance, the observed clinical improvements highlight the need for larger-scale studies to further explore mindfulness meditation's role in fertility-related anxiety management.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".