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2024_08_03_7d78e77ed465914310f4g

肿瘤专科护士胜任力潜在剖面分析及其与再培训参与动机的关系研究
Potential Analysis of Competence of Oncology Nurses and Its Relationship with Motivation for Re-training Participation

【摘要】目的 探究肿瘤专科护士胜任力的潜在类别, 并分析各类别与再培训参与动机的关系。方法 采用整群抽样方法, 选取山西省各地市肿瘤专科护士作为调查对象。采用一般资料调查表、肿瘤专科护士胜任力量表、教育参与动机量表收集资料, 进行潜在剖面分析。结果 最后共纳入 538 名符合标准的肿瘤专科护士,其胜任力分为"低胜任力水平型( )"、"中胜任力水平型( )"、"高胜任力水平型( )" 3 个潜在类别。多元 Logistic 回归分析显示:不同学历、医疗机构类别、职务的肿瘤专科护士在 3 种胜任力类别上的分布比较,差异有统计学意义( )。不同胜任力类别的肿瘤专科护士在再培训参与动机中社会服务、职业进展及逃避刺激维度得分比较, 差异有统计学意义( )。结论 肿瘤专科护士的胜任力水平多处于中胜任力水平型, 其再培训参与动机以内部驱动为主。护理管理者可根据不同类别的肿瘤专科护士特征及其与再培训参与动机的关系激发其学习动机,为构建科学合理的肿瘤专科护士再培训体系提供参考。
【Abstract】Objective To explore the potential categories of competence of oncology nurses and analyze the relationship between each category and motivation for participation in retraining. Methods Using cluster sampling method, oncology nurses from various cities in Shanxi Province were selected as the survey subjects. General information questionnaire, oncology nurse competence scale, and education participation motivation scale were used to collect data and conduct latent profile analysis. Results A total of 538 oncology nurses who met the criteria were included, and their competence was divided into three potential categories: "low competence level type ( )", "medium competence level type ( )", and "high competence level type ( )". Multivariate logistic regression analysis showed that there were statistically significant differences in the distribution of oncology nurses in the three competence categories based on different educational backgrounds, types of medical institutions, and positions ( ). There were statistically significant differences in the scores of social service, career advancement, and escape from stimulation dimensions of motivation for participation in retraining among oncology nurses in different competence categories ( ). Conclusion The competence level of oncology nurses is mostly at the medium competence level type, and their motivation for participation in retraining is mainly driven internally. Nursing managers can stimulate their learning motivation based on the characteristics of oncology nurses in different categories and their relationship with motivation for participation in retraining, providing reference for the construction of a scientific and reasonable retraining system for oncology nurses.
【关键词】肿瘤专科护士;胜任力;潜在剖面分析;再培训
【Keywords】Oncology specialist nurse; competency; potential profile analysis; retraining

A potential profile analysis of the competency of oncology nurses and its relationship with the motivation of retraining participation

【Abstract】 Objective To explore the potential categories of competency of oncology nurses, and to analyze the relationship between each category and the motivation of retraining participation.
Methods Cluster sampling was used to select oncology nurses from 11 cities in Shanxi Province as the survey subjects. The general data questionnaire, the oncology nurse competency scale, and the educational participation motivation scale were used to collect data and conduct potential profile analysis. Results A total of 538 oncology nurses who met the criteria were included, and their competency was divided into three potential categories: "low competency level (23.4%)", "medium competency level (58.6%)" and "high competency level (18.0%)". Multivariate Logistic regression analysis showed that there was a significant difference in the distribution of oncology nurses with different educational backgrounds, medical institution categories and job titles in the three competency categories ( ). There were statistically significant differences in the scores of social service, career progress, and stimulation avoidance among oncology nurses with different competency categories in the motivation to participate in retraining education ( ). Conclusion
The competency level of oncology nurses was mostly in the middle competency level, and their motivation for retraining was mainly internally driven. Nursing managers can stimulate their learning motivation according to the characteristics of different types of oncology nurses and their relationship with the motivation of retraining participation, so as to provide a reference for the construction of a scientific and reasonable retraining system for oncology nurses.
【Keywords】Oncology nurse; competency;Latent profile analysis; Retraining
【Keywords】Oncology nurse; competency; Latent profile analysis; Retraining
癌症防治是健康中国 2030 建设的重要内容。目前,中国每年新发癌症病例约为 406 万,每年癌症死亡约 241 万 。肿瘤专科护士作为癌症患者的全病程管理者,在其康复指导、疼痛管理、长期护理、营养和心理支持等方面发挥着重要作用 。患癌因素的多样性及医疗技术的革新,要求肿瘤专科护士具备高胜任力以改善癌症患者生存质量并减轻其经济负担 。现有研究表明 ,中国肿瘤专科护士胜任力处于中等偏上水平,仍需提升。再培训是维持与提高肿瘤专科护士胜任力水平的重要途径 ,而仅山东省、浙江省 对专科护士再培训与再认证有政策要求,但无具体专科方向再培训指导。教育参与动机是激发、维持并引导行为朝向学习目标的一种内部心理过程或内部心理状态 ,正向的学习动机对再培训有积极影响。查阅肿瘤专科护士开展的再培训量性研究发现 ,研究者主要对其核心能力及影响因素进行调查分析,表明再培训必要性与迫切性,尚无对影响其再培训参与动机因素及再培训内容的研究。潜在剖面分析 (latent profile analysis,LPA)使用潜变量模型来估计外显变量与潜变量之间的关系,根据个体在各个条目(外显变量)上的得分来判断个体的潜在特征分类及人口占比 。相较普通分析方法,更能充分考虑护士群体内部的异质性。鉴于此本研究采用 LPA 来识别具有不同胜任力水平的肿瘤专科护士群体类别,并探讨不同类别对再培训参与动机的差异,为护理管理者和教育者构建科学、合理的肿瘤专科护士再培训体系提供参考。
Cancer prevention and control is an important part of the Healthy China 2030 initiative. Currently, China sees about 4.06 million new cancer cases and approximately 2.41 million cancer-related deaths each year. Oncology nurses play a crucial role as the primary caregivers for cancer patients, providing guidance on recovery, pain management, long-term care, nutrition, and psychological support. The diversity of cancer risk factors and advancements in medical technology require oncology nurses to have high competency to improve the quality of life for cancer patients and alleviate their financial burden. Existing research indicates that the competency level of oncology nurses in China is above average but still needs improvement. Ongoing training is a key way to maintain and enhance the competency level of oncology nurses, yet only Shandong and Zhejiang provinces have policy requirements for specialized nurse retraining and recertification, without specific guidance on specialized training directions. Educational motivation is an internal psychological process or state that stimulates, maintains, and directs behavior towards learning goals. Positive learning motivation has a beneficial impact on retraining. Research on the quantity of retraining among oncology nurses has shown the necessity and urgency of retraining, but there is a lack of studies on factors influencing their motivation to participate in retraining and the content of retraining. Latent profile analysis (LPA) uses latent variable models to estimate the relationship between observed variables and latent variables, categorizing individuals based on their scores on various items (observed variables) to determine potential characteristics and population proportions. Compared to conventional analysis methods, LPA can better account for the heterogeneity within the nursing population. This study uses LPA to identify different categories of oncology nurses with varying competency levels and explore the differences in motivation to participate in retraining among these categories, providing a reference for nursing managers and educators to establish a scientific and rational retraining system for oncology nurses.

1 对象与方法 1 Objects and Methods

1.1 对象 1.1 Objects

采用整群抽样法,于 2023 年 3 月至 5 月选取山西省各地市符合纳入标准的 538 名肿瘤专科护士作为研究对象。纳入标准:(1)完成系统的专科护士培训,通过考核,取得中华级和(或)省级专科护士资质;(2)取得参与本研究的知情同意。排除标准:(1)调查期间因各种原因无法填写问卷;(2)取得专科护士资格证但未在临床一线工作者;(3)不在岗者。采用样本量粗略估计法计算样本量,即样本量为量表最大维度数的 倍,并考虑 的脱落率 。本研究中,肿瘤专科护士胜任力量表维度数最多,共 8 个,以 的脱落率计算,所需样本量为 144 192 例, 本研究样本量充足。本研究已通过伦理委员会批准(2023-YX-237)。
Using cluster sampling method, 538 oncology specialist nurses from various cities in Shanxi Province that meet the inclusion criteria were selected as research subjects from March to May 2023. Inclusion criteria: (1) Completion of specialized nurse training, passing the assessment, and obtaining national and/or provincial specialized nurse qualifications; (2) Obtaining informed consent to participate in this study. Exclusion criteria: (1) Unable to complete the questionnaire for various reasons during the survey period; (2) Holding a specialized nurse qualification but not working as a frontline clinical worker; (3) Not on duty. The sample size was calculated using a rough estimation method, which is the maximum number of dimensions on the scale multiplied by , and considering a dropout rate of . In this study, the oncology specialist nurse competency scale has the most dimensions, totaling 8. With a dropout rate of , the required sample size is 144,192 cases, indicating that the sample size in this study is sufficient. This study has been approved by the ethics committee (2023-YX-237).

1.2 调查工具 1.2 Survey Tools

1.2.1 一般资料调查表 1.2.1 General Information Survey Form

由研究者自行设计, 包括性别、年龄、学历、医疗机构类别、所属层级、职称资格、职务、专科护士资格证级别。
Designed by researchers themselves, including gender, age, education, type of medical institution, hierarchical affiliation, professional qualifications, position, and specialized nurse qualification certificate level.

1.2.2 肿瘤专科护士胜任力量表(Oncology Nursing Competency Self-rating Scale, ONCSS)
1.2.2 Oncology Nursing Competency Self-rating Scale (ONCSS)

由马池芬等 于 2014 年研制, 用于测量及肿瘤专科护士的专科护理能力水平。该量表包括肿瘤护理的评估与计划、肿瘤护理的实施与评价、肿瘤患者的心理社会支持、咨询、教育和管理、协调、科研、专业发展 8 个维度, 共 54 个条目。用 5 级 Likert 标度法, 标度为: "没有能力"、"有一点能力"、"有一些能力"、"有足够的能力"、"很有能力",相对应的分值为 分, 分数越高说明该护士在肿瘤专科护理岗位胜任力越高。该量表具有良好的信效度, 量表总的 Cronbach' 系数为 0.985 , 分半信度系数为 0.925 。本研究中, 该量表的 Cronbach' 系数为 0.917 。
Developed by Ma Chifen et al. in 2014, it is used to measure the specialized nursing ability level of nurses in oncology. The scale includes 8 dimensions: assessment and planning of oncology nursing, implementation and evaluation of oncology nursing, psychological and social support for oncology patients, counseling, education and management, coordination, research, and professional development, with a total of 54 items. Using a 5-point Likert scale, the scale is: "no ability", "a little ability", "some ability", "enough ability", "very capable", with corresponding scores of 1 to 5, where higher scores indicate higher competence of the nurse in the oncology nursing position. The scale has good reliability and validity, with a total Cronbach's alpha coefficient of 0.985 and a split-half reliability coefficient of 0.925. In this study, the Cronbach's alpha coefficient of the scale is 0.917.

1.2.3 教育参与动机量表((Eduction Participation Scal, EPS)
1.2.3 Education Participation Scale (EPS)

由饶钼灯等 修订加拿大鲍歇尔教授等人的研究成果上研制的用于测量医务人员学习动机的量表。该量表包括社会关系、外在期望、社会服务、职业进展、逃避与刺激、求知兴趣 6 个维度, 共 27 个条目。采用 Likert 5 级评分法进行计分, 其中 分各表示照 "不同意"、
Revised by Rao Moding and others, a scale developed based on the research results of Canadian professor Boucher for measuring the learning motivation of medical personnel. The scale includes six dimensions: social relationships, external expectations, social services, career advancement, escape and stimulation, and curiosity for knowledge, with a total of 27 items. Scoring is done using the Likert 5-point scale, where each point represents "disagree".
"有点同意" 、"基本同意"、"同意" 和 "很同意" 。分数越高, 参与再培训学习动机越高。该量表具有良好的信效度, Cronbach' 系数为 0.89 , 各维度的 Cronbach' 系数介于 之间。本研究中, 该量表的 Cronbach' 系数为 0.792 。
"Somewhat agree", "Basically agree", "Agree", and "Strongly agree". The higher the score, the higher the motivation to participate in further training. The scale has good reliability and validity, with a Cronbach's alpha coefficient of 0.89, and Cronbach's alpha coefficients for each dimension ranging between . In this study, the Cronbach's alpha coefficient of the scale is 0.792.

1.3 资料收集方法与质量控制 1.3 Data Collection Methods and Quality Control

本研究以电子问卷形式, 借助问卷星和微信平台进行问卷发放与回收。调查机构肿瘤专科护士负责人作为调查员, 研究者采用规范、统一的指导语对调查员进行培训。发放问卷前向研究对象说明研究目的、意义和填写方法, 征得其知情同意, 用智能设备在线填写问卷; 为保证研究对象符合专科护士要求, 设置身份识别问题作为身份过滤网。利用问卷自动跳转功能, 减少人工跳答导致的失误; 避免重复填写, 设置同一 IP 地址限填 1 次; 保证问卷作答的完整性,系统设置为必须填写完毕所有必填项才能成功提交问卷,研究对象在作答的任何阶段均可退出,退出不会对其工作造成任何影响。为确保数据的有效性, 剔除全部同一选项或填写时间少于 3 分钟的问卷。最终收集 589 份问卷, 回收有效问卷 538, 有效问卷回收率
This study distributed and collected questionnaires in electronic form using the Questionnaire Star and WeChat platforms. The head nurse of the oncology department served as the investigator, and the researchers provided standardized and unified guidance to train the investigators. Before distributing the questionnaires, the research subjects were informed of the research purpose, significance, and how to fill them out, and their informed consent was obtained. They filled out the questionnaires online using smart devices. To ensure that the research subjects met the requirements of specialized nurses, identity verification questions were set up as an identity filtering network. The questionnaire's automatic skip function was used to reduce errors caused by manual skipping. To avoid duplicate entries, only one entry per IP address was allowed. To ensure the completeness of questionnaire responses, the system was set up to require all mandatory fields to be completed before successfully submitting the questionnaire. Research subjects could exit at any stage of answering without any impact on their work. To ensure data validity, questionnaires with all the same options selected or completed in less than 3 minutes were excluded. In the end, 589 questionnaires were collected, with 538 valid questionnaires retrieved, resulting in a valid questionnaire recovery rate of .

1.4 统计学方法 1.4 Statistical Methods

采用 Mplus 8.3 软件进行潜在剖面分析。以 ONCSS 的 8 个维度得分为外显变量, 依次选取
Conduct latent profile analysis using Mplus 8.3 software. The scores of the 8 dimensions of ONCSS are used as manifest variables, selected in sequence.
1 5 个剖面进行分析, 最终模型的拟合效果通过以下的 3 类指标来判断。(1)信息评价指标:通过艾凯克信息准则 (AIC) 、贝叶斯信息准则(BIC)和调整贝叶斯信息准则(aBIC)比较期望值与实际值差异来判断模型拟合优劣,统计值越小表示拟合效果越好。(2)分类评价指标:通过信息熵(Entropy)评价分类的精确性,其取值范围为 ,越接近于 1 表明分类越精确。(3)似然比检验:通过罗-梦戴尔-鲁本校正似然比检验(LMR)和基于 Bootstrap 的似然比检验(BLRT)比较 个和 k 个类别模型间的拟合差异。当 LMR 和 BLRT 的 时, 表示 k 个类别模型优于 个类别模型。上述评价指标只为剖面决策提供参考, 在确定最佳模型时, 还应考虑各类别的可解释性
An analysis was conducted on 15 profiles, and the fitting effect of the final model is judged by the following three types of indicators. (1) Information evaluation indicators: The fit of the model is judged by comparing the expected value with the actual value through Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Adjusted Bayesian Information Criterion (aBIC). The smaller the statistical value, the better the fit. (2) Classification evaluation indicators: The accuracy of classification is evaluated by information entropy, with a value range of . The closer to 1, the more accurate the classification. (3) Likelihood ratio test: The fit difference between models with and k categories is compared using the Lo-Mendell-Rubin Adjusted Likelihood Ratio Test (LMR) and Bootstrap Likelihood Ratio Test (BLRT). When LMR and BLRT are , it indicates that the k-category model is better than the -category model. The above evaluation indicators are only for profile decision-making reference. When determining the best model, the interpretability of each category should also be considered.
采用 SPSS 25.0 统计软件进行数据分析, 计量资料用均数士标准差表示, 组间比较采用单因素方差分析;计数资料使用频数和百分比表示,组间比较采用卡方检验或秩和检验。以单因素分析具有统计学意义的变量作为自变量, 以潜在剖面作为因变量, 进行多元 Logistic 回归分析。检验水准 为 0.05 , 均为双侧检验, 以 为差异具有统计学意义。
Using SPSS 25.0 statistical software for data analysis, quantitative data are expressed as mean and standard deviation, and inter-group comparisons are made using one-way analysis of variance; count data are represented by frequency and percentage, and inter-group comparisons are made using chi-square test or rank sum test. Using variables with statistical significance in one-way analysis as independent variables, and latent profiles as dependent variables, conducting multivariate logistic regression analysis. The significance level is 0.05, all are two-tailed tests, and the difference at is statistically significant.

2 结果 2 Results

2.1 共同方法偏差检验 2.1 Joint Method Deviation Test

采用 Harman 单因子检验法进行共同方法偏差的检验。结果显示,共提取出 14 个特征值大于 1 的因子, 第一个因子的累积解释率为 , 低于 的临界标准, 表明不存在严重的共同方法偏差。
Using the Harman single-factor test method to test for common method bias. The results show that 14 factors with eigenvalues greater than 1 were extracted, with the cumulative explanatory rate of the first factor being , which is lower than the critical standard of , indicating the absence of serious common method bias.

2.2 肿瘤专科护士胜任力的潜在剖面分析结果 2.2 Potential Profile Analysis Results of Competence of Oncology Specialist Nurses

本研究共探索了 5 个潜在剖面模型, 见表 1。随着类别数的增加, 的绝对值、AIC、 BIC、aBIC 模型拟合指标数值逐渐降低, 3 类别是下降的拐点;根据 Entropy 指标, 3 类别时分类精确性最高;BLRT 和 LMR 显著性指标结果表明 3 类别优于 2 类别, 4 类别与 3 类别无显著差异; 各潜在类别的平均归属概率在 之间, 均高于 0.80 , 表明具有高的分类精确性。因此在模型拟合指标的基础上,考虑模型的简约性和可解释性,最终选择 3 类别模型作为肿瘤专科护士胜任力的潜在剖面分析结果。
This study explored a total of 5 potential profile models, as shown in Table 1. With the increase in the number of categories, the absolute values of , AIC, BIC, and aBIC model fit indices gradually decreased, with 3 categories being the turning point of decrease; according to the Entropy index, the classification accuracy was highest with 3 categories; BLRT and LMR significance indices indicated that 3 categories were superior to 2 categories, and there was no significant difference between 4 categories and 3 categories; the average membership probability of each latent category was between , all higher than 0.80, indicating high classification accuracy. Therefore, based on the model fit indices and considering the simplicity and interpretability of the model, the 3-category model was ultimately selected as the potential profile analysis result for the competency of oncology nurses.
在此基础上, 获得 3 个潜在剖面在 8 个外显指标的潜在剖面图, 见图 1, 根据每个潜在剖面在各维度上的得分情况来为 命名。 C 1 组的肿瘤专科护士在各维度的得分均处于较低水平,因此命名为 "低胜任力水平型",共 126 人( );C2 组在除科研维度得分较低外, 其余各维度得分较为稳定处于中等偏上水平, 因此命名为 "中胜任力水平型", 共 315 人( );C3 组在 8 个维度上的得分均处于最高水平,因此命名为"高胜任力水平型",共 97 人( )。
Based on this, 3 potential profiles were obtained in the latent profile analysis of 8 manifest indicators, as shown in Figure 1, and named according to the scores of each potential profile on each dimension. The tumor specialist nurses in Group C1 scored at a relatively low level in each dimension, so they were named "Low Competence Level Type," totaling 126 people ( ); Group C2 scored relatively stable at a medium to upper level in all dimensions except for the research dimension, so they were named "Medium Competence Level Type," totaling 315 people ( ); Group C3 scored at the highest level in all 8 dimensions, so they were named "High Competence Level Type," totaling 97 people ( ).

表 1 肿瘤专科护士胜任力的潜在剖面模型拟合指标 Table 1 Potential Profile Model Fitting Indicators of Competence of Oncology Specialist Nurses
模型 Model AIC BIC aBIC Entropy 类别概率(%) Category Probability (%)
LMR BLRT
7985.011 8045.041 8000.601
7426.349 7520.682 7450.846 0.740
7170.165 7298.801 7203.571 0.828 0.001
7069.166 7232.105 7111.480 0.804 0.208
7010.491 7207.732 7061.713 0.800 0.212
图 1 肿瘤专科护士胜任力的潜在剖面特征 Figure 1 Potential Profile Features of Competence of Oncology Specialist Nurses
2.3 不同组别肿瘤专科护士胜任力的一般人口学特征
2.3 General demographic characteristics of tumor specialist nurses in different groups

2.3.1 三组肿瘤专科护士的一般人口学资料比较 2.3.1 Comparison of general demographic data of three groups of tumor specialist nurses

单因素分析结果显示, 不同学历、医疗机构类别、所属层级及职务的分布不同, 差异具有统计学意义( )。详见表2。
The results of the single-factor analysis show that there are statistically significant differences in the distribution of different educational levels, types of medical institutions, hierarchical levels, and positions ( ). See Table 2 for details.
表 2 不同人口学特征的肿瘤专科护士胜任力类别比较
Table 2 Comparison of Competence Categories of Tumor Specialist Nurses with Different Demographic Characteristics
项目 Project

Low competency level
低胜任力水平型

Competency Level Type
中胜任力水平型

High competency level type
高胜任力水平型
性别 Gender 0.751 0.812
年龄 Age
  years old
  years old
  years old
学历 Education
专科及以下 Junior college and below
本科 Undergraduate
硕士及以上 Master's degree or above
医疗机构类别 Category of medical institutions
三甲医院 Third-grade hospital
非三甲医院 Non-tertiary hospital
280 (88.9)

所属层级 Hierarchy Level

N1
N2
N3
N4

职称资格 Professional title qualification

职务 Duty
病区护士长 Ward Nurse Manager
科护士长及以上 Chief nurse and above
专科护士证书等级 Associate Degree in Nursing Certificate Level
省级 Provincial
中华级  Chinese level
9 (7.1)
2 (1.6)
5 (4.0)
163 (51.7)
248 (78.7)
78 ( 80.4
257 (81.6)
45 (14.3)
18 (18.6)

2.3.2 肿瘤专科护士胜任力的影响因素的多元 logistic 回归分析
2.3.2 Multivariate logistic regression analysis of factors influencing the competence of oncology nurses

以肿瘤专科护士胜任力的潜在剖面作为因变量,"低胜任力水平型"为参考组,自变量赋值方式见表 3。将单因素分析中的指标作为自变量纳入多元 Logistic 回归中。结果显示,学历为本科( ,医疗机构为三甲医院( [1.080 14.972]),职务为病区护士长( )的护士更有可能归于高胜任力水平型。肿瘤专科护士胜任力 3 个类别在所属层级上差异无统计学意义( )。详见表4。
Using the potential profile of competency of oncology specialist nurses as the dependent variable, with the "low competency level type" as the reference group, the assignment method of the independent variables is shown in Table 3. The indicators from the univariate analysis were included as independent variables in the multivariate logistic regression. The results show that nurses with a bachelor's degree ( ), working in a tertiary hospital ( [1.080 14.972]), and holding the position of ward nurse manager ( ) are more likely to belong to the high competency level type. There was no statistically significant difference in the competency of oncology specialist nurses across the three categories at the hierarchical level ( ). See Table 4 for details.
表 3 自变量赋值 Table 3 Variable Assignment
变量 Variable 赋值 Assignment
学历 Education 专科及以下 ;本科 研究生及以上=3
Associate degree and below ; Bachelor's degree Graduate and above=3
医疗机构类别 Category of medical institutions 三甲医院 非三甲医院=2 Tertiary hospitals non-tertiary hospitals = 2
所属层级 Hierarchy Level 以" 为参照设置哑变量 Set dummy variables with " " as reference
哑变量 , "  Dummy variable , " "
哑变量 , N  Dummy variable , N
哑变量 , "  Dummy variable , " "
无=1; 病区护士长=2; 科护士长及以上=3 None=1; Ward Nurse Manager=2; Department Nurse Manager and above=3
表 4 肿瘤专科护士胜任力影响因素的多元 Logistic 回归
Table 4 Multivariate Logistic Regression of Factors Influencing the Competence of Oncology Nurses
自变量 Independent variable {
高胜任力水平型 vS
低胜任力水平型
}
{
High competency level type vS
Low competency level
}
{
中胜任力水平型 vs
低胜任力水平型
}
{
Competency Level Type vs
Low competency level
}
OR OR95% CI OR OR95% CI
学历(对照组=专科及以下) Education (control group = college degree and below)
本科 Undergraduate -0.712 -0.520
医疗机构类别(对照组=非三甲医院) Medical institution category (control group = non-tertiary hospital)
三甲医院 Third-grade hospital 1.391 -0.045 0.956
职务(对照组 无职务) Position (Control Group No Position)
病区护士长 Ward Nurse Manager -1.968 -0.753 0.471
注: *表示  Note: * indicates

2.4 不同胜任力类别肿瘤专科护士的再培训参与动机得分比较
2.4 Comparison of motivation scores for retraining participation among tumor specialist nurses in different competency categories

研究结果显示,不同胜任力类别肿瘤专科护士在再培训参与内部动机与外部动机差异有统计学意义( )。各维度中, 3 个类别在社会服务、职业进展及逃避刺激维度差
Research results show that there is a statistically significant difference in the internal and external motivation of tumor specialist nurses in different competency categories participating in retraining. In each dimension, the three categories differ in social service, career advancement, and stimulus avoidance dimensions.

异有统计学意义  There is statistical significance .
表 5 不同胜任力类别肿瘤专科护士的再培训参与得分比较 (分, )
Table 5 Comparison of scores of participation in retraining of tumor specialist nurses in different competency categories (points, )
变量 Variable C 1 C 2 C 3 F P 两两比较 Compare pairwise
社会关系 Social relationships 0.701 0.497
外在期望 External Expectations 0.584 0.558
社会服务 Social Services 6.980
职业进展 Career Progression