Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample
Abstract Background Psychologically traumatic childbirth leads to short and long-term negative impacts on a woman’s health and impacts future reproductive decisions. Considering the importance of fertility growth and strengthening positive fertility motivations in …, this community-based study was c...
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| Language: | English |
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BMC
2025-02-01
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| Series: | Reproductive Health |
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| Online Access: | https://doi.org/10.1186/s12978-025-01952-z |
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| author | Mahdieh Arian Talat Khadivzadeh Mahla Shafeei Sedigheh Abdollahpour |
| author_facet | Mahdieh Arian Talat Khadivzadeh Mahla Shafeei Sedigheh Abdollahpour |
| author_sort | Mahdieh Arian |
| collection | DOAJ |
| description | Abstract Background Psychologically traumatic childbirth leads to short and long-term negative impacts on a woman’s health and impacts future reproductive decisions. Considering the importance of fertility growth and strengthening positive fertility motivations in …, this community-based study was conducted to investigate the relationship between traumatic childbirth history and positive and negative fertility motivations. Methods The present cross-sectional study was conducted on 900 women of reproductive age. Sampling lasted from March 21 to September 23, 2023, using multi-stage and convenient sampling from health-treatment centers in …. History of pregnancy and childbirth, DSM-A criterion, and Miller’s questionnaire were used to collect data. For data analysis, Python software was used for machine learning and elastic net analysis was conducted in a nested cross-validation framework. Results Of the 900 women participating in this study, 387 reported a history of traumatic birth and 513 reported no history of traumatic birth. The positive and negative fertility motivations have a significant relationship with the previous history of traumatic childbirth. Elastic network modeling predicts using RMSE, MAE and R-squared that religious beliefs, married duration, and women’s education have the greatest increasing effect on positive fertility motivation. Drug addiction, traumatic childbirth, and abortion history have the greatest effect on increasing negative fertility motivation. Conclusions Positive and negative fertility motivations are significantly affected by the history of traumatic childbirth. Therefore, in countries that want to grow their population, preventing traumatic childbirth and providing counseling interventions should be placed in the priorities of maternal care. |
| format | Article |
| id | doaj-art-d4605ecdc9604044b772e84c30095e49 |
| institution | Kabale University |
| issn | 1742-4755 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Reproductive Health |
| spelling | doaj-art-d4605ecdc9604044b772e84c30095e492025-02-09T12:39:46ZengBMCReproductive Health1742-47552025-02-0122111010.1186/s12978-025-01952-zMachine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sampleMahdieh Arian0Talat Khadivzadeh1Mahla Shafeei2Sedigheh Abdollahpour3Reproductive Health, Nursing and Midwifery Care Research Center, Mashhad University of Medical SciencesReproductive Health, Nursing and Midwifery Care Research Center, Mashhad University of Medical SciencesReproductive Health, Nursing and Midwifery Care Research Center, Mashhad University of Medical SciencesReproductive Health, Nursing and Midwifery Care Research Center, Mashhad University of Medical SciencesAbstract Background Psychologically traumatic childbirth leads to short and long-term negative impacts on a woman’s health and impacts future reproductive decisions. Considering the importance of fertility growth and strengthening positive fertility motivations in …, this community-based study was conducted to investigate the relationship between traumatic childbirth history and positive and negative fertility motivations. Methods The present cross-sectional study was conducted on 900 women of reproductive age. Sampling lasted from March 21 to September 23, 2023, using multi-stage and convenient sampling from health-treatment centers in …. History of pregnancy and childbirth, DSM-A criterion, and Miller’s questionnaire were used to collect data. For data analysis, Python software was used for machine learning and elastic net analysis was conducted in a nested cross-validation framework. Results Of the 900 women participating in this study, 387 reported a history of traumatic birth and 513 reported no history of traumatic birth. The positive and negative fertility motivations have a significant relationship with the previous history of traumatic childbirth. Elastic network modeling predicts using RMSE, MAE and R-squared that religious beliefs, married duration, and women’s education have the greatest increasing effect on positive fertility motivation. Drug addiction, traumatic childbirth, and abortion history have the greatest effect on increasing negative fertility motivation. Conclusions Positive and negative fertility motivations are significantly affected by the history of traumatic childbirth. Therefore, in countries that want to grow their population, preventing traumatic childbirth and providing counseling interventions should be placed in the priorities of maternal care.https://doi.org/10.1186/s12978-025-01952-zElastic netMachine learningTraumatic childbirthBirth traumaFertility motivation |
| spellingShingle | Mahdieh Arian Talat Khadivzadeh Mahla Shafeei Sedigheh Abdollahpour Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample Reproductive Health Elastic net Machine learning Traumatic childbirth Birth trauma Fertility motivation |
| title | Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample |
| title_full | Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample |
| title_fullStr | Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample |
| title_full_unstemmed | Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample |
| title_short | Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample |
| title_sort | machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in iran in a community based sample |
| topic | Elastic net Machine learning Traumatic childbirth Birth trauma Fertility motivation |
| url | https://doi.org/10.1186/s12978-025-01952-z |
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