Background and Purpose: Researchers’ incomplete perception of the concerns about childbearing decision making process is revealed in discussions about policies and programs that are designed to influence fertility. P...Background and Purpose: Researchers’ incomplete perception of the concerns about childbearing decision making process is revealed in discussions about policies and programs that are designed to influence fertility. Perceiving the concerns of women is essential to explain process of decision making for childbearing. This study aimed to understand women’s main concerns about childbearing decision making. Methods: This qualitative study was performed by conventional content analysis approach. The participants included 22 married women in Tehran who were pregnant for the first time or were using contraceptive methods. Purposeful sampling began and continued up to data saturation. To collect data, the unstructured in-depth interviews were used. Data were analyzed using qualitative content analysis by Lundman and Graneheim method. Findings: Four categories were obtained from data including “fear”, “uncertainty”, “hope” and “financial security”. The main category or theme was “concerns about one’s own future or securing child’s future” that was extracted as the main concern of women about childbearing decision making process. Conclusion: The findings of this study suggest that concerns about one’s own future or securing child’s future are the main concerns of women about childbearing decision making. Deep understanding of women’s concerns about childbearing will help midwives and other service providers to provide services, strategies and more sensitive and appropriate interventions.展开更多
数据包络分析(data envelopment analysis,DEA)在为决策单元(decision making unit,DMU)评估效率水平的同时,可为其中的非有效单元提供消除低效的改进措施,即基准信息。但经典DEA模型为非有效单元提供的基准信息不易一步到位,缺乏对分...数据包络分析(data envelopment analysis,DEA)在为决策单元(decision making unit,DMU)评估效率水平的同时,可为其中的非有效单元提供消除低效的改进措施,即基准信息。但经典DEA模型为非有效单元提供的基准信息不易一步到位,缺乏对分组信息的充分利用。在依赖上下文的DEA框架内进行开发,提出了一种基于分组的两步DEA基准学习模型。模型使用加权L1范式衡量待评估单元与相应目标的接近程度。通过最小化实际点到Pareto有效边界的距离,为每一个决策单元在组内和全局的最佳实践前沿上分别设立单独基准,解决了在实践中目标点难以一步实现的问题,模型的结果可以视为针对最佳实践的长期改进策略。由于充分考虑了分组信息,模型能够反映给定基准过程中涉及的DMU周围环境,并增强了组内DMU在设立目标上的灵活性。该模型被用于评估西班牙公立大学的科研水平,通过对比实验验证了该模型的优势。展开更多
文摘Background and Purpose: Researchers’ incomplete perception of the concerns about childbearing decision making process is revealed in discussions about policies and programs that are designed to influence fertility. Perceiving the concerns of women is essential to explain process of decision making for childbearing. This study aimed to understand women’s main concerns about childbearing decision making. Methods: This qualitative study was performed by conventional content analysis approach. The participants included 22 married women in Tehran who were pregnant for the first time or were using contraceptive methods. Purposeful sampling began and continued up to data saturation. To collect data, the unstructured in-depth interviews were used. Data were analyzed using qualitative content analysis by Lundman and Graneheim method. Findings: Four categories were obtained from data including “fear”, “uncertainty”, “hope” and “financial security”. The main category or theme was “concerns about one’s own future or securing child’s future” that was extracted as the main concern of women about childbearing decision making process. Conclusion: The findings of this study suggest that concerns about one’s own future or securing child’s future are the main concerns of women about childbearing decision making. Deep understanding of women’s concerns about childbearing will help midwives and other service providers to provide services, strategies and more sensitive and appropriate interventions.
文摘数据包络分析(data envelopment analysis,DEA)在为决策单元(decision making unit,DMU)评估效率水平的同时,可为其中的非有效单元提供消除低效的改进措施,即基准信息。但经典DEA模型为非有效单元提供的基准信息不易一步到位,缺乏对分组信息的充分利用。在依赖上下文的DEA框架内进行开发,提出了一种基于分组的两步DEA基准学习模型。模型使用加权L1范式衡量待评估单元与相应目标的接近程度。通过最小化实际点到Pareto有效边界的距离,为每一个决策单元在组内和全局的最佳实践前沿上分别设立单独基准,解决了在实践中目标点难以一步实现的问题,模型的结果可以视为针对最佳实践的长期改进策略。由于充分考虑了分组信息,模型能够反映给定基准过程中涉及的DMU周围环境,并增强了组内DMU在设立目标上的灵活性。该模型被用于评估西班牙公立大学的科研水平,通过对比实验验证了该模型的优势。
