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（
【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 (
【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 (
【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
【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 (
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
【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
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.
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
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
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
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.
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
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.
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
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".
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'
"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
"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
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
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.
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
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
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
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
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
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
2 结果 2 Results
2.1 共同方法偏差检验 2.1 Joint Method Deviation Test
采用 Harman
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
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
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,
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
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
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
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 (
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
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 (
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 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（
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 (
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 (
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
Table 2 Comparison of Competence Categories of Tumor Specialist Nurses with Different Demographic Characteristics
| 项目 Project | Low competency level
|
Competency Level Type
|
High competency level type
|
|
|
||||||
| 性别 Gender | 0.751 | 0.812 |
| 男 |
|
|
|
| 女 |
|
|
|
年龄 Age
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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
无
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
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（
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 (
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 (
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
Associate degree and below |
| 医疗机构类别 Category of medical institutions | 三甲医院
|
| 所属层级 Hierarchy Level | 以"
|
| 哑变量
|
|
| 哑变量
|
|
| 哑变量
|
|
| 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
Table 4 Multivariate Logistic Regression of Factors Influencing the Competence of Oncology Nurses
| 自变量 Independent variable | {
{
|
{
{
|
|||||||||||||
|
|
OR | OR95%
|
|
OR | OR95%
|
||||||||||
| 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
|
|||||||||||||||
| 病区护士长 Ward Nurse Manager | -1.968 |
|
|
-0.753 | 0.471 |
|
|||||||||
注: *表示
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（
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
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
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,
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 |
|