Ajou University College of Nursing, Neuro Intensive Care Unit, Ajou University Hospital, Suwon, South Korea.
College of Nursing, Research Institute of Nursing Science, Ajou University, Suwon, South Korea.
Asian Nurs Res (Korean Soc Nurs Sci). 2024 Feb;18(1):36-43. doi: 10.1016/j.anr.2024.01.005. Epub 2024 Jan 27.
This study explored the work adjustment trajectory and its predictors and characteristics among newly registered nurses.
A total of 245 newly registered nurses working in a university hospital provided general baseline characteristics and completed a work adjustment questionnaire along with self-report measures of clinical competency, psychological capital, preceptor exchange, social support, and role conflict when they started working independently (baseline) and at 7 and 12 months after employment. Data were collected from July 2020 to August 2022. The collected data were subjected to a group-based trajectory model, χ2 test, F test, one-way ANOVA, and multiple logistic regression using SAS 9.4, and SPSS 25.0.
Group-based trajectory modeling classified three newly registered nurse groups: nurses with a high work adjustment level in all subscales from the beginning of employment (early adjustment group, 16.1%), nurses with a moderate level of adjustment from beginning to end (standard adjustment group, 60.6%), and nurses with a low level of work adjustment from early to mid-term, rising later (delayed adjustment group, 23.3%). Higher hope, optimism, and emotional support predicted early and standard adjustments.
Based on the trajectory characteristics, newly registered nurses need to improve their work adjustment. The early and standard adjustment groups should continuously monitor their levels of work adjustment while monitoring their hopes, optimism, and emotional support. In particular, the delayed adjustment group required customized educational programs and strengthened peer support.
本研究探讨了新注册护士的工作调整轨迹及其预测因素和特征。
共有 245 名在一所大学医院工作的新注册护士提供了一般基线特征，并在开始独立工作（基线）以及就业后 7 个月和 12 个月时，完成了工作调整问卷以及临床能力、心理资本、导师交流、社会支持和角色冲突的自我报告测量。数据收集时间为 2020 年 7 月至 2022 年 8 月。使用 SAS 9.4 和 SPSS 25.0 对收集的数据进行基于群组的轨迹模型、χ2 检验、F 检验、单向方差分析和多逻辑回归分析。
基于群组的轨迹建模将三组新注册护士进行分类：从一开始就所有子量表的工作调整水平较高的护士（早期调整组，16.1%）、从开始到结束调整水平中等的护士（标准调整组，60.6%）和从早期到中期工作调整水平较低，后来上升的护士（延迟调整组，23.3%）。更高的希望、乐观和情感支持预测了早期和标准调整。
根据轨迹特征，新注册护士需要提高他们的工作调整能力。早期和标准调整组应在监测希望、乐观和情感支持的同时，不断监测其工作调整水平。特别是，延迟调整组需要定制教育计划并加强同伴支持。
