Chinese Journal of Nursing ›› 2022, Vol. 57 ›› Issue (17): 2068-2072.DOI: 10.3761/j.issn.0254-1769.2022.17.003

• Special Planning——Respiratory Disease Nursing • Previous Articles     Next Articles

An investigation of developmental trajectory of unmet needs in lung cancer patients undergoing chemo-therapy

WANG Ting(), ZHANG Jing(), YU Miao   

  • Received:2022-03-29 Online:2022-09-10 Published:2022-09-07
  • Contact: ZHANG Jing

肺癌化疗患者未满足需求发展轨迹的调查研究

王婷(), 张静(), 于淼   

  1. 150001 哈尔滨市 哈尔滨医科大学附属第四医院护理部
  • 通讯作者: 张静
  • 作者简介:王婷:女,硕士,护师,E-mail: 309572979@qq.com
  • 基金资助:
    黑龙江省自然科学基金联合引导项目(LH2021H039)

Abstract:

Objective To explore the latent class in developmental trajectory of unmet needs of lung cancer patients during chemotherapy and the different characteristics of each potential category. Methods A total of 226 lung cancer patients were sampled from the oncology wards of 4 tertiary hospitals in Harbin and Xi’an from September 2019 to June 2020 using a convenient sampling method. The patients were followed up for 3 times,and the latent class growth model was used to analyze the data. Results 5 latent growth trajectories for unmet needs were identified,namely the high-needs rise group(11.5%),the medium-needs stable group(32.3%),the high-needs stable group(43.7%),the low-needs rise group(6.6%),and the high-needs decrease group(5.8%). There were differences in demographic information,disease-related data,psychological factor and quality of life among the groups. Conclusion Clinical staff should make the individualized intervention according to the developmental trajectory of unmet needs of patients.

Key words: Lung Cancer, Unmet Needs, Latent Class Growth Model, Oncology Nursing

摘要:

目的 探讨肺癌化疗患者未满足需求发展轨迹的潜在类别,分析不同类别组的特征差异。 方法 2020年9月—2021年6月,采用便利抽样法选取哈尔滨市、西安市、北京市4所三级甲等医院住院化疗的肺癌患者226例,对其进行3次追踪调查,采用潜类别增长模型进行数据分析。 结果 获得5个潜类别轨迹组:高需求上升组(11.5%)、中需求平稳组(32.3%)、高需求平稳组(43.7%)、低需求上升组(6.6%)、高需求下降组(5.8%),各组在人口学、疾病相关资料、心理因素以及生活质量方面具有差异性(P<0.05)。 结论 肺癌化疗患者未满足需求存在个体差异,护理人员应重视并实施动态评估,根据患者的不同特征及时给予针对性的支持护理。

关键词: 肺癌, 未满足需求, 潜类别增长模型, 肿瘤护理