Strategies to Mitigate Anxiety and Pain in Intrauterine Device Insertion: A Systematic Review
Bibliographic record
Abstract
One barrier to the use of intrauterine devices (IUDs) as a contraceptive method is the experience of anxiety and pain during the insertion procedure. Previous reviews have focused on pharmacological methods used to relieve pain during IUD insertion; however, few similar reviews have examined non-pharmacological methods to relieve pain or strategies to reduce anxiety. The objectives of this study were to identify and categorize strategies for reducing anxiety and pain with respect to IUD insertion and the ways in which anxiety and pain were assessed. In particular, the study aimed to identify non-pharmacological interventions and studies that included anxiety as a research outcome. A literature search was conducted of all English-language studies between inception and the week of July 29, 2018 from the following online databases: Medline, Embase, Cochrane Library, and PubMed. The search revealed 426 studies after removal of duplicates, 35 of which fulfilled the inclusion criteria. A total of 29 studies were identified as assessing pharmacological interventions for the management of pain, and six studies assessed non-pharmacological interventions. Only one study included a measurement of patient anxiety during the procedure as an outcome measure. Research on non-pharmacological interventions for the management of anxiety and pain during IUD insertion is lacking. This review found that evidence for the studied pharmacological interventions is conflicting, and there is very little evidence on understanding the effectiveness of strategies to manage anxiety during the IUD insertion procedure. Further high-quality research on non-pharmacological pain and anxiety management strategies is warranted.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".