探索护理专业学生的自尊变化及其预测因素:队列研究及其方法论挑战。

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引用次数: 0

摘要

目的:考虑到方法上的困难和以往相关研究结果的不一致,本研究旨在利用合理的理论框架和相关工具测量和分析数据,描述护生自尊随时间的变化及其预测因素:方法:队列研究。在2017年至2019年的每学年初和2020年5月学年结束时,使用塔法罗迪和斯旺的自我喜欢/自我胜任量表修订版,对四年制本科护理专业学生的自尊和相关因素进行评估。通过广义线性混合模型进行了单变量和多变量分析:计划开始时,自尊心处于中等水平:50.40 (9.10).随着时间的推移,自尊略有上升:T1 为 51.15 (9.11);T2 为 51.64 (0.49);T3 为 51.39 (10.79)(p = .002)。预测自尊变化的基线变量是性别、中学毕业水平、状态焦虑、继续学习的意愿和自我效能感。通过强有力的职业规划来评估和改善最后这些变量,可以提高学生的成绩,并增加培训期间和培训后的保留率:护理专业学生的自尊可通过双因素量表(如 Tafarodi 和 Swann 的自我喜欢/自我胜任量表)的二维模型进行评估。广义线性混合模型是关联大量纵向数据的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring nursing student self-esteem change and its predictors: Cohort study and its methodological challenges

Objective

Considering methodological difficulties and inconsistent results of previous studies on it, the objective was to describe the change in nursing student self-esteem over time and its predictors, using a sound theoretical framework and relevant tools to measure and analyse the data.

Method

Cohort study. Self-esteem and related factors were assessed in nursing students in a 4-year bachelor’s program at the beginning of each academic year from 2017 to 2019 and at the end of the academic year in May 2020, using he revised version of Self-liking/Self-competence scale of Tafarodi and Swann. Univariate and multivariate analyses were performed via generalized linear mixed modelling.

Results

Self-esteem was moderate at the beginning of the program: 50.40 (9.10). It increased slightly over time: 51.15 (9.11) at T1; 51.64 (0.49) at T2; and 51.39 (10.79) at T3 (P = .002). Baseline variables that predicted self-esteem change were gender, secondary school graduation level, state anxiety, intent to continue, and self-efficacy. Assessing and improving those last variables via a strong career plan could boost student achievement and increase retention during and after training.

Conclusion

Nursing student self-esteem can be assessed using a bidimensional model, with a two-factor scale like Tafarodi & Swann’s self-liking/self-competence scale. Generalized linear mixed model is a useful way to correlate large sets of longitudinal data.

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