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Abstract Background During pregnancy and childbirth, alongside positive feelings, women undergo feelings such as fear of childbirth (FoC) and worry about its consequences, which could leave negative effects on the mother and her child during pregnancy, delivery, and postpartum. The study was carried out to determine the effectiveness of prenatal non-pharmacological interventions on reducing the FoC. Methods The protocol of the study was registered in PROSPERO (ID: CRD42023468547). PubMed, Web of Science, Cochrane, Scopus, SID (Scientific Information Database) and Google Scholar search engine databases were systematically searched until July 27, 2023 with no limitation of time and limited to Persian and English studies in order to perform this overview. Certainty of evidence was assessed using GRADE, methodological quality using AMSTAR 2 and reporting quality using PRISMA score. Meta-analysis was performed on the data extracted from the original trials to evaluate the effect of different interventions on reducing the FoC. Sub-group analysis and meta-regression models were used to examine high heterogeneity, and sensitivity analysis was used to eliminate the effect of high risk of bias studies on the study findings. Results Overall, 15 systematic reviews (SRs) were included in the overview, among which meta-analysis was performed in 9 studies. Considering methodological quality, these SRs were in low to critically low status and had relatively complete reports regarding reporting quality. Meta-analysis findings indicated that psychological interventions (SMD -2.02, 95% CI -2.69 to -1.36, 16 trials, 1057 participants, I2 = 95%) and prenatal educations (SMD -0.88, 95% CI -1.16 to -0.61, 4 trials, 432 participants, I2 = 72.8%) cause a significant reduction in FoC relative to prenatal usual cares with low certainty of evidence. Distraction techniques lead to a significant reduction in FoC relative to prenatal usual care with high certainty of evidence (SMD -0.75, 95% CI -1.18 to -0.33, 4 trials, 329 participants, I2 = 69%), but enhanced cares do not result in a significant decrease FoC relative to prenatal usual care with very low certainty of evidence (SMD -1.14, 95% CI -2.85 to 0.58, 3 trials, 232 participants, I2 = 97%). Conclusions Distraction techniques are effective in reducing FoC. Regarding the effect of psychological interventions and prenatal educations on the reduction of FoC, the findings indicated that the interventions may result in the reduction of FoC. Very uncertain evidence showed that enhanced cares are not effective in reducing the FoC.

Chain-of-thought prompting enhances the abilities of large language models (LLMs) significantly. It not only makes these models more specific and context-aware but also impacts the wider field of artificial intelligence (AI). This approach broadens the usability of AI, increases its efficiency, and aligns it more closely with human thinking and decision-making processes. As we improve this method, it is set to become a key element in the future of AI, adding more purpose, precision, and ethical consideration to these technologies. In medicine, the chain-of-thought prompting is especially beneficial. Its capacity to handle complex information, its logical and sequential reasoning, and its suitability for ethically and context-sensitive situations make it an invaluable tool for healthcare professionals. Its role in enhancing medical care and research is expected to grow as we further develop and use this technique. Chain-of-thought prompting bridges the gap between AI's traditionally obscure decision-making process and the clear, accountable standards required in healthcare. It does this by emulating a reasoning style familiar to medical professionals, fitting well into their existing practices and ethical codes. While solving AI transparency is a complex challenge, the chain-of-thought approach is a significant step toward making AI more comprehensible and trustworthy in medicine. This review focuses on understanding the workings of LLMs, particularly how chain-of-thought prompting can be adapted for nephrology's unique requirements. It also aims to thoroughly examine the ethical aspects, clarity, and future possibilities, offering an in-depth view of the exciting convergence of these areas. [ABSTRACT FROM AUTHOR]

Chain-of-thought prompting enhances the abilities of large language models (LLMs) significantly. It not only makes these models more specific and context-aware but also impacts the wider field of artificial intelligence (AI). This approach broadens the usability of AI, increases its efficiency, and aligns it more closely with human thinking and decision-making processes. As we improve this method, it is set to become a key element in the future of AI, adding more purpose, precision, and ethical consideration to these technologies. In medicine, the chain-of-thought prompting is especially beneficial. Its capacity to handle complex information, its logical and sequential reasoning, and its suitability for ethically and context-sensitive situations make it an invaluable tool for healthcare professionals. Its role in enhancing medical care and research is expected to grow as we further develop and use this technique. Chain-of-thought prompting bridges the gap between AI’s traditionally obscure decision-making process and the clear, accountable standards required in healthcare. It does this by emulating a reasoning style familiar to medical professionals, fitting well into their existing practices and ethical codes. While solving AI transparency is a complex challenge, the chain-of-thought approach is a significant step toward making AI more comprehensible and trustworthy in medicine. This review focuses on understanding the workings of LLMs, particularly how chain-of-thought prompting can be adapted for nephrology’s unique requirements. It also aims to thoroughly examine the ethical aspects, clarity, and future possibilities, offering an in-depth view of the exciting convergence of these areas.