Search

Aim: This Study Aimed to Assess the Intention to Have a Third Child among Millennial Parents (25–40 years old) with Two Children in a City in Eastern China and to Explore the Influencing Factors Related to Fertility Intention. Design: A cross‐sectional design study. Methods: A convenience sampling method was used to enrol participants of childbearing age who visited two tertiary hospitals in Hang zhou, a city in eastern China, from June 2021 to March 2022. We conducted a face‐to‐face questionnaire survey with 520 participants and calculated the prevalence of intention‐related factors. Multivariate logistic regression was used to analyse the independent influencing factors of fertility intention. Results: In total, 105 (20.2%) participants had the intention to have a third child. The results showed that 'employment status', 'age', 'reasons for wanting a third child', the considered 'biggest barrier to having a third child', 'views on the three‐child policy', 'desired free services', 'supporting work policies' and 'assistance policies' were significant independent influencing factors of intention to have a third child (p‐value < 0.05). The intention of the participants 'over 30 years old' was 2.466 times that of those '30 years old and under', and 'older age/personal health status' was considered the 'biggest barrier to having a third child'. Regarding policy and social reasons, the participants who need 'medical assistance' policy negatively affect the intention to have a third child (OR = 0.453, 95% CI = 0.247–0.830). Implications for Healthcare/Nursing: Nursing plays an important role in health promotion. Nurses can help couples make wise decisions about fertility by providing professional consultation, education, evaluation and support. They can also provide corresponding nursing and guidance to improve couples' health quality and overall reproductive success. Conclusions: The general level of intention to have a third child of Millennial parents with two children is still low. The participants who are 'housewives/househusbands', 'over 30 years old', and satisfied with the state of 'medical assistance' have higher fertility intentions. Patient or Public Contribution: It is particularly meaningful for the policymakers to improve the social support system and raise universal awareness to encourage childbirth. [ABSTRACT FROM AUTHOR]

Ecologists and evolutionary biologists are regularly tasked with the comparison of binary data across groups. There is, however, some discussion in the biostatistics literature about the best methodology for the analysis of data comprising binary explanatory and response variables forming a 2 × 2 contingency table.We assess several methodologies for the analysis of 2 × 2 contingency tables using a simulation scheme of different sample sizes with outcomes evenly or unevenly distributed between groups. Specifically, we assess the commonly recommended logistic (generalised linear model [GLM]) regression analysis, the classical Pearson chi‐squared test and four conventional alternatives (Yates' correction, Fisher's exact, exact unconditional and mid‐p), as well as the widely discouraged linear model (LM) regression.We found that both LM and GLM analyses provided unbiased estimates of the difference in proportions between groups. LM and GLM analyses also provided accurate standard errors and confidence intervals when the experimental design was balanced. When the experimental design was unbalanced, sample size was small, and one of the two groups had a probability close to 1 or 0, LM analysis could substantially over‐ or under‐represent statistical uncertainty. For null hypothesis significance testing, the performance of the chi‐squared test and LM analysis were almost identical. Across all scenarios, both had high power to detect non‐null effects and reject false positives. By contrast, the GLM analysis was underpowered when using z‐based p‐values, in particular when one of the two groups had a probability near 1 or 0. The GLM using the LRT had better power to detect non‐null results.Our simulation results suggest that, wherever a chi‐squared test would be recommended, a linear regression is a suitable alternative for the analysis of 2 × 2 contingency table data. When researchers opt for more sophisticated procedures, we provide R functions to calculate the standard error of a difference between two probabilities from a Bernoulli GLM output using the delta method. We also explore approaches to compliment GLM analysis of 2 × 2 contingency tables with credible intervals on the probability scale. These additional operations should support researchers to make valid assessments of both statistical and practical significances. [ABSTRACT FROM AUTHOR]