ReviewHealthcare (Basel, Switzerland)2024
Digital Training for Nurses and Midwives to Improve Treatment for Women with Postpartum Depression and Protect Neonates: A Dynamic Bibliometric Review Analysis.
Review in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Intermittent Preventive Treatment of Malaria in Pregnancy and the Impact on Neonates in African Countries as Assessed by Entropy Weight and TOPSIS Methods.Journal of clinical medicine · 2024Article
- Bibliometric Analysis on of the Impact of Screening to Minimize Maternal Mental Health on Neonatal Outcomes: A Systematic Review.Journal of clinical medicine · 2024Review
- Evaluating Malnutrition Practices and Mother's Education on Children Failure to Thrive Symptoms Using Entropy-Weight and TOPSIS Method.Children (Basel, Switzerland) · 2024Article
- Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland.International journal of environmental research and public health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The high prevalence of postpartum depression makes it necessary for midwives and nurses to implement prenatal interventions for expectant mothers. The current study aims to investigate and highlight the importance of the digital training of nurses in order to help women mitigate the symptoms of postpartum depression and protect infants. To approach this, we conducted a bibliometric analysis to address the study's main objective. Articles were retrieved from the Scopus database for the timeframe 2000-2023. Data analysis was conducted using the statistical programming language R (version R-4.4.) and the bibliometric software VOSviewer (version 1.6.20) and Biblioshiny (version 4.1.4), focused on year, journal, and country. For this investigation, we selected a total of 31 MeSH keywords and sub-headings that exhibited significant frequencies. We consistently used six significant clusters of MeSH keywords. We obtained a total of 585 articles from the Scopus database that were major contributors to the field of PPD, as evidenced by their extensive publication of research articles and their influential role in the domain. The studies included a thorough analysis of depression research, the use of scales for diagnosing and screening PPD, psychological studies related to PPD, and the exploration of causes, mechanisms, outcomes, and genetic factors. Our study's results demonstrate a steady and significant increase in the availability of information on PPD. Importantly, the novelty of the current study lies in highlighting the need for a transition in the ways in which nurses and midwives are trained to mitigate postpartum disease by integrating emerging technologies into their practices. The knowledge provided here has the potential to serve as a foundation for future advancements in obstetric psychology, both presently and in the future.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.