Effectiveness and influencing factors of standardised training for nursing assistants: a cross-sectional study
- 1Department of Nursing, Fujian Health College, Fuzhou, China
- 2Xiamen Medical College, Xiamen, Fujian, China
- Correspondence to Ying Chen; chenying_yc{at}126.com
Abstract
Objectives To investigate the impact of standardised training on nursing assistants (NAs) and explore the factors influencing training effectiveness, specifically focusing on learning motivation and learning obstacles.
Design A cross-sectional study was conducted from August 2022 to September 2022.
Setting The study was conducted in Fujian Province, China.
Participants A total of 412 participants were included in this study.
Primary and secondary outcome measures Training effectiveness, learning motivation, learning obstacles and learning engagement were the primary outcome measures.
Methods A survey was conducted on research NAs who were participating in standardised training in Fujian Province, China. The survey used general information questionnaires, educational participation scales, general suitability scales for participation barriers, blended learning participation scales and training effectiveness questionnaires.
Results The overall score of the training effectiveness questionnaires was 98.08±15.52, with an average score per item of 4.26±0.67. Univariate analysis revealed significant associations between the number of working years, self-rated health status, acceptance of NAs work and training effectiveness (p<0.05). Multiple linear regression analysis indicated that learning motivation, learning obstacles and learning engagement were independent factors influencing training effectiveness (F=171.073, p<0.001).
Conclusion Learning motivation, learning obstacles and learning engagement all affect training effectiveness, which provides a clear direction for improving the effectiveness of training for NAs. In order to improve the training effectiveness of healthcare aides, it is necessary for clinical managers to focus on their learning motivation, learning obstacles and learning engagement.
- Cross-Sectional Studies
- EDUCATION & TRAINING (see Medical Education & Training)
- Nursing research
Data availability statement
Data are available upon reasonable request. Data are available from Ying Chen (email: chenying_yc@126.com) upon reasonable request.
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STRENGTHS AND LIMITATIONS OF THIS STUDY
Research focuses on the motivational factors and barriers that influence the participation of nursing assistants(NAs) in continuing education.
This study investigates the impact of demographic factors on the effectiveness of standardised training for NAs.
The sample size was limited to one province, which may not provide a comprehensive representation of NAs in China.
Introduction
The current shortage of nurses has become a global health issue.1 According to the State of the World’s Nursing 2020 Report,2 it is estimated that there will be a worldwide deficit of 5.7 million registered nurses (RNs) by 2030. As of the end of 2020, the total number of RNs in China surpassed 4.7 million, with 3.35 RNs for every 1000 people,3 but this was lower than the global average of 3.816 RNs per 1000 people.4 In response to this nursing shortage, developed countries in Europe, as well as the USA, have taken the proactive measure of establishing medical nursing assistants (NAs). These allied health professionals provide basic care and support services under the supervision of licensed nursing staff.5 These NAs play an essential role in delivering healthcare services and serve as indispensable members of society.
In China, there is a severe shortage of NAs. To address the staffing shortfall and attract more individuals to engage in medical work in the short term, the entry requirements for caregivers are currently minimal, with no academic qualifications required. Most caregivers are unemployed urban or rural workers who have been transferred from other positions. These practitioners tend to be older, possess lower levels of education, lack systematic vocational training, exhibit poor overall quality and stability within their career teams and experience low rates of licensed employment.6 Thus, their knowledge base, skill sets and overall quality often fail to meet patient care needs.
Multiple studies have consistently demonstrated that the absence of scientific and effective job training, as well as continuing education, renders medical care providers more susceptible to causing hospital infections and medical accidents due to boundary violations and improper procedures, jeopardising patient safety.7 8 This risk directly impacts patients’ lives and quality of life. Furthermore, inadequate education and training impede the job satisfaction of medical care providers, resulting in a higher turnover rate.9 10 Consequently, there is an urgent need for standardised and systematic on-the-job training for these professionals.
Some studies consider adult learning motivation a prerequisite for individuals to sustain their engagement in lifelong learning, highlighting that adult learners with high motivation and a desire for success are more inclined to actively participate in lifelong learning activities.11 Previous research has confirmed the significant role of learning motivation in adult participation in lifelong learning.11 The term ‘participation barrier’ refers to the difficulties or challenges encountered during educational activities after expressing an intention to participate.12 The Fourth Global Report on Adult Learning and Education13 categorises barriers that prevent participation in adult learning and education into situational barriers (traditional cultural barriers, lack of time, family responsibilities, etc), institutional barriers (lack of infrastructure, high costs, etc) and dispositional barriers (attitudes and willingness to participate in learning). Most healthcare workers are unemployed in rural areas, with low education levels, older age and belonging to the marginalised group of adult learning and education. To effectively promote the transformation of marginalised groups from ‘marginalised participation’ to ‘full participation’, it is necessary to understand the main obstacles these groups are facing to provide targeted support and help.
Currently, most research pertaining to the training of these professionals primarily focuses on surveys assessing their training needs, exploration of developing comprehensive training systems and evaluating satisfaction levels with existing programmes. However, there is a dearth of research investigating the motivating factors and barriers influencing their participation in continuing education. This study aims to comprehend the effects of training on medical care providers and explore influencing factors, investigating both facilitating (motivational) and inhibiting (barrier-related) aspects of adult learning, thereby providing insights for developing more scientifically grounded and applicable training programmes.
Methodology
Patient and public involvement
The study did not involve patients. All data for this study were obtained from NAs. Neither study participants nor the public participated in the design, conduct, reporting or dissemination plans of our research.
Study design
This study used a cross-sectional correlational design.
Setting and participants
This study used convenience sampling to recruit NAs who were participating in standardised training in Fujian Province, China, from August to September 2022.
The inclusion criteria for the participants were as follows: (1) age ≥18 years; (2) currently working as an NA; (3) completed the full-time standardised medical caregiver training course in Fujian Province and participated in the assessment (the total number of hours of standardised training is 120, with each lesson lasting no less than 45 min); (4) no cognitive or communication barriers and (5) provided informed consent and volunteered to participate in this study.
The exclusion criteria were as follows: (1) less than 3 months of experience working as an NA; (2) medical caregivers involved in non-clinical frontline duties and (3) individuals who took leave for more than 1 week during the training period due to personal or medical reasons.
Sample size and sampling procedure
The sample size was computed by referring to the sample demand for multiple linear regression analysis,14 which should be at least 10 times the quantity of independent variables. In this research, the number of independent variables was 14. Taking into account the possibility of 10%–20% invalid questionnaires, the ultimate sample content was set to be 155–175 cases. A sum of 442 questionnaires were gathered; 412 were valid (with a 93.2% valid return rate); 30 responses were either incomplete or invalid and were excluded, as elaborated in a diagram presented in figure 1.
Figure 1
Diagram regarding the sampling process.
Variables and instruments
The English version of the Chinese scale is presented in online supplemental file 1. These research instruments have good reliability and validity with a reasonable number of entries, which are suitable for measuring healthcare workers’ learning motivation, learning obstacles, learning engagement and training effectiveness.
Supplemental material
General information questionnaire
A general information questionnaire was developed by our research team based on the research objectives and content derived from a comprehensive literature review. The questionnaire comprised 14 demographic items: gender, age (years), educational level, marital status, number of children, years worked as a NA, religious beliefs, number of participants with previous systematic training, NA category, the main reason for becoming a NA, self-rated health status, workplace, monthly income per capita of the family (Yuan) and acceptance of NA work.
Education participation scale
Boshier15 developed the Education Participation Scale in 1971, which was translated into Chinese and revised by Rao et al16 and has good reliability and validity. This scale has 6 dimensions and 27 items. This scale uses a 5-point Likert scale ranging from 1 to 5, where higher scores indicate stronger levels of learning motivation. The score for this scale ranges between 27 and 135 points. The scale has a Cronbach’s α coefficient of 0.89, with each dimension ranging from 0.47 to 0.84, indicating good reliability across all dimensions.
Deterrent to participate scale-general (DPS-G)
Scanlan and Darkenwald developed the General Participatory Deterrence Scale (DPS-G),12 which was revised and finalised by Darkenwald and Valentine.17 The final version was translated into Chinese by Wang and Chen18 in 2009. DPS-G consists of six dimensions, using a 5-point Likert scale ranging from 1 to 5, with higher scores indicating greater deterrents to participation. The Cronbach’s α coefficient of the entire scale was 0.94, with that of each dimension ranging from 0.76 to 0.89, indicating strong reliability and validity suitable for implementation.
Hybrid learning engagement scale
The Hybrid Learning Engagement Scale developed by Kong19 comprises five dimensions (learning challenge, learning participation, diverse interaction, strategic use and environmental support) and includes a total of 23 items. This tool uses a 5-point Likert scale ranging from 1 to 5. The overall Cronbach’s α coefficient is 0.98, and each subscale exhibits a high degree of internal consistency. The Cronbach’s α coefficient for each subscale represents 0.934 for learning challenge, 0.932 for learning participation, 0.942 for diverse interaction, 0.961 for strategic use and 0.953 for environmental support.
Training effectiveness questionnaire
We have designed a self-developed ‘Training Effectiveness Questionnaire’ to quantitatively assess training effectiveness. The questionnaire comprises five dimensions: basic knowledge (five items), basic skills (four items), personal traits (five items), communication skills (four items) and awareness of laws and regulations (five items), totalling 23 items. This self-assessment tool uses a 5-point Likert scale ranging from 1 (no improvement at all) to 5 (significant improvement). The total score ranges from 23 to 115, with higher scores indicating better training effectiveness. The overall reliability was 0.972, and the reliabilities of the dimensions of basic knowledge, basic skills, personal traits, communication skills and awareness of laws and regulations were 0.902, 0.910, 0.886, 0.875 and 0.895, respectively. In terms of scale validity, χ2/df=3.621, Root Mean Square Error of Approximation (RMSEA)=0.080, Comparative Fit Index (CFI)=0.931, Tucker-Lewis Index (TLI)=0.920 and Standardized Root Mean Square Residual (SRMR)=0.032. All model fit indices met the requirements, indicating good structural validity of the scale.
Data collection
The survey questionnaire was distributed using a combination of online and offline methods. Trained and experienced investigators collected paper questionnaires. After obtaining informed consent, a one-on-one approach was adopted for on-site investigation. For research subjects with lower education levels, investigators maintained a neutral attitude by strictly adhering to the content and order of the questions. Investigators avoided altering or adding personal emotions during the process of recording oral responses from research subjects to maintain the authenticity of the data. Electronic questionnaires were generated using Questionnaire Star (a tool for questionnaire surveys) and distributed with the assistance of training unit personnel. Respondents could only proceed after agreeing to provide informed consent. All questions in the electronic questionnaire were mandatory to ensure the completeness of the research results. In addition, each electronic questionnaire is equipped with measures to restrict respondents from submitting multiple times in order to avoid affecting the authenticity of the results. A total of 442 paper and electronic questionnaires were collected. Questionnaires with a completion time of <360 s and excessive similarity in responses were excluded. Finally, a total of 412 valid questionnaires were retained. After the collection of the questionnaire, two researchers entered and inspected it using Epidata V.3.1 software to guarantee the validity and completeness of the questionnaire.
Statistical analysis methods
The data were recorded and checked by two researchers. SPSS V.24.0 software was used for data analysis. Measurement data are reported as means and standard deviation(SD) for descriptive statistics, whereas counting data are reported as frequencies and percentages. Independent-sample t-tests and one-way Analysis of Variance (ANOVA) were used to analyse the training effect. Pearson correlation analysis was used to explore correlations among training effect, learning obstacles and learning motivation. The factors influencing the training effect were analysed with multiple linear regression. If there is missing data, the mean-filling method was used to process the missing values. A p<0.05 was considered a statistically significant difference.
Results
General information of the research subjects
A total of 412 research participants were enrolled in this study, including 152 male (36.9%) and 260 female (63.1%) participants. The mean age of the participants was 49.63±6.80 years, ranging from 24 to 67 years. 87.6% of NAs have an education level of junior high school or below. 41.3% of the NAs have less than 1 year of work experience. 51.9% of NAs have excellent physical health. Only 0.7% of NAs expressed that they strongly disliked the job of being an NA, as detailed in table 1.
Table 1
Analysis of training effect under different variables
Scores of training effectiveness, learning motivation, learning obstacles and learning engagement
The scores for training effectiveness, learning motivation, learning obstacles and learning engagement were calculated, including the total scores and the mean of items for each scale or questionnaire dimension. The training effectiveness questionnaire had a total score of 98.08±15.52 with a mean item score of 4.26±0.67. The basic skills dimension exhibited the highest mean item score (4.30±0.74), whereas the communication ability dimension exhibited the lowest mean item score (4.20±0.73). The educational involvement scale had a total score of 100.33±18.41 with a mean item score of 3.72±0.68. The career advancement dimension had the highest mean item score (4.17±0.64), whereas avoidance or stimulation had the lowest mean item score (3.16±0.94). The general participation barrier scale had a total score of 56.85±27.55, with an average score per question of 1.83±0.89. Time constraint was the dimension with the highest average score per question (2.01±1.05), whereas low interest in learning had the lowest average score per question (1.71±0.96). The mixed learning engagement scale had a total score of 101.42±16.73, with an average score per question of 4.06±0.67; environmental support had the highest average score per question (4.21±0.70), whereas multiple interactions had the lowest average score per question (3.86±0.80).
Results of the single-factor analysis on training effectiveness
Independent-sample t-tests and one-way ANOVA tests were conducted to investigate potential differences in training effectiveness across demographic variables, as shown in table 1. The results indicated that there were no statistically significant differences in training effectiveness based on gender, age, marital status, number of children, religious beliefs, previous experience with systematic training, caregiver category, primary reason for becoming a healthcare provider, work location or average monthly household income (p>0.05). However, caregivers’ educational background exhibited a statistically significant difference in training effectiveness (F=5.124, p<0.01). Caregivers with elementary school education or below exhibited the lowest scores in terms of training effectiveness compared with those with junior high school education, who scored significantly lower than those with associate’s/bachelor’s degrees. Moreover, years of experience also demonstrated a statistically significant difference in training effectiveness (F=3.372, p<0.05). Caregivers with less than 1 year of experience had significantly lower scores compared with those with 1–3, 5–10 and >10 years’ worth of experience. Self-perceived health status also exhibited a statistically significant difference in training effectiveness (F=4.199, p<0.01). Caregivers who rated their own health as ‘not very good’ or ‘average’ had significantly lower scores compared with those who rated their health as ‘good’ or ‘very good.’ A significant disparity in training effectiveness score was observed among nursing staff with varying levels of acceptance of NA work (F=6.667, p<0.001). Notably, nursing staff who possessed an average or relatively favourable level of job satisfaction exhibited significantly lower training effectiveness scores compared with those who derived high enjoyment from their work.
We performed a correlation analysis to examine the relationships between learning motivation, learning obstacles, learning engagement and training effectiveness to preliminarily assess the validity of our research hypotheses. Through a Pearson correlation analysis, we explored the associations among the measured variables. Significant positive correlations were observed between training effectiveness and both learning motivation and learning engagement (r=0.363 to 0.738, p<0.001), whereas a significant negative correlation existed between training effectiveness and learning obstacles (r=−0.103, p<0.05).
Results of the multiple linear regression analysis
Multiple linear regression analysis was conducted to further investigate the impact of various factors on training effectiveness, as detailed in table 2. The dependent variable was the score representing training effectiveness, and the independent variables included education level, years of experience in nursing work, self-rated health condition and acceptance level towards nursing work (all identified as statistically significant variables from the single-factor analysis), as well as learning motivation, learning obstacles and learning investment (all identified as statistically significant variables from the correlation analysis). The selection of independent variables followed a stepwise approach using multiple linear regression. Refer to table 3 for details on relevant variables influencing training effectiveness.
Table 2
Multiple linear regression analysis of factors influencing training effectiveness
Table 3
Assignment table of variables related to factors influencing training effectiveness
The results of the multiple linear regression analysis indicated that learning motivation, learning obstacles and learning engagement were independent factors that significantly influenced training effectiveness. The model’s R2 value was 0.557, with an F-statistic of 171.073 (p<0.001). Among these factors, learning engagement had the highest impact on training effectiveness (b=0.697), followed by learning motivation (b=0.092), whereas learning obstacles had a negative impact (b=−0.091). Table 2 provides detailed information on the multiple linear regression analysis of factors influencing training effectiveness.
Discussion
This study aimed to investigate the efficacy of standardised training for NAs, explore its influencing factors and provide a theoretical foundation for enhancing the quality of healthcare training. The results indicated that the participants exhibited moderate levels of learning motivation (mean item score=3.72±0.68), encountered minimal barriers to learning (mean item score=1.83±0.89), demonstrated high engagement in mixed learning environments (mean item score=4.06±0.67) and experienced positive training outcomes (mean item score=4.26±0.67). Univariate analysis revealed significant associations between years of nursing work experience, self-rated health status, acceptance level towards nursing work, and training effectiveness.
Multivariate linear regression analysis demonstrated that demographic factors had no impact on training effectiveness; instead, independent influencing factors included learning motivation, barriers to learning and engagement. The results suggested that the following 14 variables did not significantly affect the efficacy of the training programme, such as gender, age and educational level. This finding deviates from conclusions drawn by other scholars in their studies. Li et al20 reported that education level significantly influenced training effectiveness for elder caregivers.
The total score of the universal scale for participation barriers was 56.85±27.55, with an average score per item of 1.83±0.89. Compared with the mean score of 3 points per item, the results indicate a relatively low level of overall participation barriers among the research subjects in this training programme. One potential explanation is that the training was guided by national policies, and local governments provided robust support in terms of training facilities, faculty expertise, financial resources and qualification recognition. For example, not only was the training provided free of charge, but the medical caregivers also received three meals and accommodation during the training period. Additionally, those who successfully passed the assessment were eligible for certain financial subsidies. The employers of NAs actively comply with policy requirements to facilitate their participation in such training. Consequently, there exists fewer obstacles for research subjects to engage in this particular training. Another possible explanation is that the training was conducted online and offline. Online learning has time flexibility and convenient attendance, and offline learning can answer questions and interact with teachers and students, which further breaks learning barriers. According to Bin Mubayrik,21 acknowledging the benefits, convenience and success may increase adult learners’ acceptance of distance learning as an alternative learning approach and lead to higher enrolment. This study also partially confirms that mixed online and offline learning can be used for training adult learners in the workplace.
The dimension with the highest average score per item was time constraints (2.01±1.05). This is consistent with other previous findings that time is often seen as an important factor hindering participation in adult education.22 23 The possible reason is that this standardised training for NAs is a combination of online and offline learning, and offline learning requires two consecutive weeks of off-duty learning, which not only has a great impact on the working hours and income of NAs but also may cause dissatisfaction among patients and their families because they prefer NAs to provide continuous care services. It is worth noting that if the training duration is too short or the content is not comprehensive enough, it may undermine the effect of on-the-job education. Therefore, such training programmes need to be designed to balance time, quality and the needs of participants to ensure that they meet the requirements of NAs’ professional development without interfering too much with the normal flow of care.
The multiple-factor analysis indicated a significant negative correlation between learning outcomes and learning barriers, suggesting that higher levels of learning barriers are associated with poorer training effectiveness. The contextual obstacles faced by NAs, such as economic conditions, family situations, geographical location, educational background, experience and qualifications, occupational status, awareness of labour market opportunities, the value of higher education certificates, job expectations and employer support profoundly influence their participation in learning activities.
Many empirical studies have shown that learning engagement is an important predictor of students’ academic success, classroom performance, retention and academic resilience.24–26 Therefore, promoting learning engagement is a crucial goal in teaching and learning practices. In this study, the total score of the blended learning engagement scale was 101.42±16.73, with an average score per item of 4.06±0.67. Compared with the mean score of 3 points for each item, these results indicate a relatively high level of learning engagement among the research participants in the context of blended instruction. One possible reason may be that the vocational colleges undertaking this standardisation training have a highly responsible and service-oriented faculty, providing significant assistance to students through class advisors and teachers. On the other hand, the results also demonstrate the significant role played by universities in vocational skills training and lifelong learning education services. The dimension with the lowest average score was ‘diverse interaction’ (3.86±0.80), indicating a lower level of communication and collaboration among NAs. One possible reason is that NAs perceive trainers as authoritative figures and are hesitant to discuss or interact with them. Additionally, some of the training was conducted online, with many older students having lower levels of education, which affects their acceptance and use of smart devices and online learning apps, making online interaction even more challenging. Existing research has shown that perceived ease of use increases learners’ intention to use information technology.27 However, if learners encounter unresolved technological barriers during their learning process, it can hinder their acceptance and usage of information technology. Therefore, it is recommended that training institutions or vocational colleges provide guidance on how to use online platforms before conducting online training sessions for students.
It is necessary for clinical managers to build a policy system that closely links the effectiveness of training with career advancement paths so as to stimulate the internal motivation of NAs to learn. In terms of clinical practice, the pretraining period should accurately analyse the learning barriers, tailor the adaptive programme and, at the same time, adopt multiple teaching paradigms to strengthen the degree of participation in learning so as to achieve the optimisation of the training effect.
However, this study has some limitations. First, the sample size was limited to one province, which did not take into account the differences in the influence of relevant factors on NAs in different regions, whereas the economy, welfare policies and cultural traditions of different regions may significantly affect their role perceptions, workloads, psychological states, etc. Subsequent studies may extend the scope to regions with different backgrounds to conduct a comprehensive assessment and carry out a multicentre study to increase the sample size. Second, this study focused on NAs in general without distinguishing between those caring for patients, elderly individuals or infants. Further research should delve into a more detailed analysis of different categories of NAs.
Conclusion
Learning motivation, learning obstacles and learning engagement all affect training effectiveness, which provides a clear direction for improving the effectiveness of training for NAs. In order to improve the training effectiveness of healthcare aides, it is necessary for clinical managers to focus on their learning motivation, learning obstacles and learning engagement.
Data availability statement
Data are available upon reasonable request. Data are available from Ying Chen (email: chenying_yc@126.com) upon reasonable request.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study was approved by the Medical Ethics Committee of Fujian Health College (RT2024-04). Participants gave informed consent to participate in the study before taking part.
Acknowledgments
The authors thank all the participants for their cooperation and the nursing assistants of the cooperative hospitals for their support in this study.
