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定义护理工作量预测因素:一项试点研究初步分析

2022-01-10 15:32

护理工作量对医疗机构很重要。它决定了护士的幸福感和护理质量。然而,其预测因素几乎没有被研究。

Abstract

Aim

To explore predictors of perceived nursing workload in relation to patients, nurses, and workflow.

Background

Nursing workload is important to healthcare organizations. It determines nurses’ well-being and quality of care. Nevertheless, its predictors are barely studied.

Methods

Cross-sectional prospective design based on the Complex Adaptive Systems theory was used. An online survey asked nurses to describe perceived workload at the end of every shift. Data were gathered from five medical-surgical wards over three consecutive weeks. We received 205 completed surveys and tested multivariate regression models.

Results

Patient acuity, staffing resources, patient transfers, documentation, patient isolation, unscheduled activities, and patient specialties were significant in predicting perceived workload. Nurse-to-patient ratio proved not to be a predictor of workload.

Conclusions

This study significantly contributed to literature by identifying some workload predictors. Complexity of patient care, staffing adequacy and some workflow aspects were prominent in determining the shift workload among nurses.

Implications for Nursing Management

Our findings provide valuable information for top and middle hospital management, as well as for policymakers. Identification of predictors and measurement of workload are essential for optimizing staff resources, workflow processes, and work environment. Future research should focus on the appraisal of more determinants.

摘要翻译(仅供参考)

目的

探讨与病人、护士和工作流程有关的感知护理工作量的预测因素。

背景

护理工作量对医疗机构很重要。它决定了护士的幸福感和护理质量。然而,其预测因素几乎没有被研究。

研究方法

采用基于复杂适应性系统理论的横断面前瞻性设计。一项在线调查要求护士在每班结束时描述感知的工作量。在连续三周内从五个内外科病房收集数据。我们收到了205份完成的调查,并测试了多变量回归模型。

结果

病人急性期、人员资源、病人转移、文件记录、病人隔离、计划外活动和病人专科在预测感觉到的工作量方面很重要。护士与病人的比例证明不是预测工作量的因素。

结论

本研究通过确定一些工作量预测因素,对文献作出了重大贡献。病人护理的复杂性、人员配备的充分性和一些工作流程在决定护士的轮班工作量方面非常突出。

护理管理的启示

我们的研究结果为医院高层和中层管理人员以及政策制定者提供了宝贵的信息。识别预测因素和测量工作量对于优化人员资源、工作流程和工作环境至关重要。未来的研究应着重于对更多决定因素的评估。

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nurses,预测因素,工作流程,人员资源,护理,护士

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