Outcomes and Associated Factors of Induction of Labor in East Gojjam Zone, Northwest Ethiopia: A Multicenter Cross-Sectional Study
Bibliographic record
Abstract
Background. Induction of labor is the initiation of uterine contractions by artificial methods once the fetus has reached viability and prior to spontaneous onset of labor with the aim of achieving vaginal delivery. Although induction of labor is a critical life-saving intervention that potentially reduces adverse pregnancy outcomes, sometimes it has undesirable consequences for the health of the mother and/or the fetus. Hence, this study aimed to evaluate the outcomes and associated factors of labor induction. Methods. An institution-based cross-sectional study was conducted from February 25 to May 25, 2020, among women undergoing induction at East Gojjam zone public hospitals in northwest Ethiopia. A structured interviewer-administered questionnaire was used to collect data from a sample of 411 mothers who were selected using a systematic random sampling technique. Stata/se™ Version 14 statistical software was used to analyze the data. Multivariable binary logistic regression was used to determine the potential factors affecting successful labor induction. Adjusted odds ratios with their 95% CI intervals were used to declare the strength of the association, and a variable with <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>p</a:mi> </a:math> value <0.05 was considered to have statistical significance. Results. The prevalence of successful induction of labor was 70.3% (65.6, 74.7). The favorable Bishop score ((CI 3.90, 1.63–9.29); <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>p</c:mi> </c:math> value = 0.002), the intermediate Bishop score ((CI 3.53, 2.15–5.82); <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>p</e:mi> </e:math> value = 0.001), labor induction using oxytocin with cervical ripening ((CI 2.60, 1.21–5.63); <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:mi>p</g:mi> </g:math> value = 0.015), and urban residence ((CI 0.48, 0.30–0.78); <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" id="M5"> <i:mi>p</i:mi> </i:math> value = 0.003) were associated with successful induction of labor. Conclusion. These findings strongly suggest that cervical conditions are important determinants for the success of labor induction. Therefore, healthcare providers should confirm the favorability of the cervical status (using Bishop score) as a strict prerequisite before actual labor induction, and special consideration should be given to those pregnant women who reside in urban areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".