The Effect of Deep Breathing Relaxation Technique on Anxiety in New Acceptors of IUD Contraception at Kandui Community Health Center
Bibliographic record
Abstract
Reproductive health is a crucial component in improving the quality of life and well-being of the community. Contraception plays a significant role in this effort. Many new IUD users experience anxiety before IUD insertion due to uncertainty surrounding the procedure and concerns about pain and side effects. This can impact patient comfort and the success of the contraceptive program. Therefore, simple and effective interventions are needed to reduce pre-procedure anxiety. This study aimed to determine the effect of deep breathing relaxation techniques on the anxiety levels of new IUD users at the Kandui Community Health Center. The study used a quasi-experimental design with a pretest–posttest approach and a control group. A total of 32 new IUD users were divided into two groups. Anxiety levels were measured using the Hamilton Anxiety Rating Scale (HARS). Analysis was performed using paired t-tests and independent t-tests with a significance level of p < 0.05 . The results showed that the experimental group experienced a significant decrease in anxiety, from an average score of 28.25 to 17.81 (p < 0.001). Meanwhile, the control group showed no significant change, from 27.94 to 26.88 (p = 0.187). The intergroup comparison test in the posttest also showed a significant difference (p < 0.001), with the experimental group in the mild anxiety category while the control group remained in the moderate category. The conclusion of this study is that the deep breathing technique is proven effective in reducing anxiety levels in new acceptors before IUD insertion.
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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 | 0.002 |
| 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 | 0.001 |
| 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".