Comparison of the Effects of Using Physiological Methods and Accompanying a Doula in Deliveries on Nulliparous Women's Anxiety and Pain
Bibliographic record
Abstract
Childbirth is a great moment in a woman's life and is inevitably influenced by emotional, social, and psychological stress. This study aimed to assess the anxiety and pain level of nulliparous women giving birth using physiological methods (without doula support) during labor and those women supported by a doula at Towhid Hospital of Jam, Bushehr, Iran in 2015. In this interventional study, 150 women were randomly assigned to either an intervention (with doula support) or a control group (with no doula support). The intrapartum, postpartum, and hidden anxiety levels were measured using Spielberger standard questionnaire used for assessing anxiety. The labor pain rate was evaluated using McGill questionnaire. Results showed that the average rate of obvious anxiety during labor was 57.76 ± 9.57 in physiological delivery (without doula) and 48.04 ± 9.61 in doula-supported delivery. The difference between mean scores of obvious anxiety during labor was significant. The mean anxiety of the control group (who did not receive doula support) was higher (P = .000). Also, the difference between the mean labor pain scores of the 2 groups was statistically significant. The results of the study showed that doula's presence has positive significant effects on labor pain and anxiety reduction; also, doula-supported mothers reported considerably lower pain and anxiety compared with those experiencing physiological delivery (without doula). Thus, the increased use of doula in hospitals all over the country is recommended.
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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 | 0.003 |
| 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".