Clinical nurses’ evidence-based healthcare competence and associated factors: a regional cross-sectional study

Abstract

Background

Evidence-Based Healthcare is essential for enhancing the quality of care and improving patient outcomes in contemporary healthcare. As the largest professional group in the healthcare sector, nurses are critical in all phases of the EBHC process. The purpose of the study was to investigate clinical nurses’ evidence-based healthcare competence and explore the associated factors.

Methods

From July to October 2023, a cross-sectional study was conducted with 531 clinical nurses recruited from hospitals in Guangdong Province, China, utilizing convenience sampling. All the collected data were analyzed descriptively, and independent samples t-test, adjusted t-test, one-way ANOVA, Welch ANOVA, Pearson’s correlation test, and multiple linear regression were employed to explore the associated factors.

Results

In total, 531 valid questionnaires were collected. The average item score of evidence-based healthcare competence among these nurses was (2.92 ± 0.78) points. A multiple linear regression analysis revealed that English literature reading ability, frequency of literature reading, workload, the number of types of evidence-based training programs they participated in, and whether they had applied for patents influenced the evidence-based healthcare competence of clinical nurses.

Conclusion

Clinical nurses exhibit varying levels of evidence-based healthcare competence, with strengths in evidence transfer and implementation but room for improvement in evidence generation and synthesis. The identified factors provide valuable insights for developing targeted interventions.

Clinical trial number

Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12912-025-03722-5.

Background

In the contemporary healthcare landscape, Evidence-Based Healthcare (EBHC) has become a cornerstone for elevating the quality of care and improving patient outcomes [1]. The entire EBHC process encompasses the critical phases of evidence generation, synthesis, transfer, and implementation, as outlined in the JBI Model of EBHC [2]. The four phases are interlinked and form a dynamic, cyclical process that continuously adapts to meet the evolving needs of global healthcare. Concretely, global healthcare priorities, as identified by health professionals and patients, are targeted by generating research evidence that is not only effective but also feasible, appropriate, and meaningful to various populations, cultures, and settings. This evidence is methodically gathered, assessed, synthesized, and distributed to healthcare providers and settings, where it is utilized and scrutinized for its impact on health outcomes, healthcare systems, and professional practice [2].

As the largest professional group within the healthcare sector, nurses play a pivotal role in the various phases of the EBHC process [3]. They have significantly participated in the entire process of EBHC through conducting primary and secondary research, such as clinical studies and systematic reviews, developing clinical practice guidelines, disseminating research evidence, and implementing evidence in their daily work [4]. However, the effectiveness of their contributions hinges upon their possession of a specific skill set consistent with the evolution of research methodologies and evidence synthesis and implementation sciences. A lack of such competencies can initiate a sequence of negative consequences, beginning with the generation of low-quality evidence, followed by its poor synthesis, dissemination, and finally, implementation. This sequence may result in the wastage of resources and the delivery of care that is not well-founded, potentially disrupting the positive reinforcement essential to the EBHC ecosystem [5]. Given the critical role and expanding involvement of clinical nurses in the EBHC process, it is essential and necessary to evaluate their competencies across the entire EBHC process.

In the field of healthcare, the significance of EBHC competence among nurses has gained widespread recognition [6]. Some studies have explored the competencies of nurse leaders and advanced practice nurses in relation to the entire EBHC process and associated factors [7, 8]. Clinical nurses, who occupy a crucial position as direct caregivers and implementers of evidence into practice, have also been the focus of some studies [9–12]. These studies have examined the EBHC competence of clinical nurses from multiple dimensions, such as knowledge, attitudes, behaviors, skills, and beliefs. Despite their value in assessing the status of EBHC competence among clinical nurses and identifying associated factors through the use of various instruments, these studies often failed to adopt a full-view perspective [9–12]. Specifically, for one thing, they tend to concentrate on isolated or limited phases of the entire EBHC process, such as evidence implementation, synthesis, and transfer [9–12]. For another, even within examined phases, their measurement instruments typically capture only partial competency components [13] or rely on overly broad criteria rather than specific, well-defined competence criteria [5]. For instance, evidence generation competence is often operationalized simplistically through single-item “research skills” measures, Such single-item measurement may result in inadequate construct representation, failing to comprehensively assess all facets of evidence generation competence [10]. Based on the above, this dual incompleteness manifests both in incomplete coverage across EBHC phases and in the failure to adequately measure all essential competencies within each phase, which may lead to a situation where reported influencing factors (e.g., training, educational level) reflect relationships constrained by these measurement gaps, rather than accurately capturing the spectrum of variables linked to EBHC competence.

Existing evidence suggests that EBHC competence among clinical nurses is associated with multi-level factors [14, 15]. Kirkpatrick’s model highlights the role of training in competency development [16]. In addition, prior studies have identified associations between EBHC competence and various factors operating at both individual and organizational levels. At the individual level, key factors include educational level [13, 15, 17], years of experience [15], professional title [17], proficiency in English [18], literature reading frequency [13], scientific research activities and achievements, such as the first or correspondent authorship of published journal papers, being a project leader of scientific research projects, participating in scientific research projects, and achieving scientific research awards or patents [10, 15, 19]. Additionally, having received Evidence-Based Nursing (EBN) training [15, 19], and being involved in evidence implementation programs have been associated with higher EBHC competence levels [13, 17]. Organizational-level factors encompass hospital grade [14, 17], department [14], workload [20], and job satisfaction [20]. However, given that prior assessments show limitations like fragmented EBHC phase coverage and incomplete competence measurement, these factors remain to be validated regarding their associations with EBHC competence.

Given the growing emphasis on the EBHC movement and the increased recognition of clinical nurses as key agents of change, it is vital to conduct a comprehensive and systematic assessment of their EBHC competence from a task-performing perspectives to measure the endpoint and identify the influencing factors of EBHC competence. Such an assessment will not only systematically shed light on the strengths and weaknesses of clinical nurses’ current competencies but also inform the development of targeted interventions and support systems designed to enhance their competence. Ultimately, this endeavor holds the promise of improving patient outcomes and advancing the overall quality of healthcare services.

Methods

Aims

The objective of this study was to examine clinical nurses’ EBHC competence and identify the associated factors.

Design

This study employed a descriptive cross-sectional design and followed the reporting recommendations outlined in the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines.

Participants

The survey was conducted in Guangdong Province, China, spanning from July to October 2023. It involved clinical nurses from diverse hospitals, who participated through the method of convenience sampling. In this study, the multiple linear regression model incorporated 14 independent variables. With the significance level (α) set at 0.05 and statistical power (1–β) at 0.90, a minimum sample size of 189 clinical nurses was calculated using PASS 15.0 software. This calculation accounted for a potential 10% dropout rate. The inclusion criteria for this study were as follows: registered nurses, currently on duty, and participants who were fully informed and voluntarily agreed to participate. Nurses leaders were excluded in the study.

Measurements

Demographic and professional characteristics

The study collected demographic data (age, gender, marital status, number of children) alongside professional characteristics such as educational background, hospital grade, department, working years, professional title, workload, job satisfaction, English literature reading ability, the frequency of literature reading, whether you have published articles in public journals, whether you have experience in obtaining research projects, whether you have applied for patents, the number of types of evidence-based training programs you participated in (including scientific research training, systematic review training, guideline training, evidence summary training, evidence transfer training, and evidence implementation training), and whether you have participated in evidence implementation programs.

EBHC competence

The EBHC competence instrument was developed and validated by our research group to assess EBHC competence [21, 22]. The development of the instrument involved a two-round Delphi procedure on a preliminarily version, which was constructed based on a theoretical framework of JBI Model of EBHC, a rapid review of systematic reviews on relevant instruments and research group discussions. Subsequently, we performed a pilot study to assess item comprehension and linguistic clarity, followed by psychometric validation with reliability and validity evaluation to finalize the instrument, which includes 47 items covering four EBHC dimensions: evidence generation (17 items), evidence synthesis (12 items), evidence transfer (four items), and evidence implementation (14 items). The instrument employs a five-point Likert scale (ranging from one = Strongly Disagree to five = Strongly Agree), where a higher total score signifies a higher level of EBHC competence. The instrument’s Cronbach’s α values for each dimension range from 0.856 to 0.965, indicating excellent internal consistency. Split-half reliability values range from 0.785 to 0.878. Exploratory factor analysis reveals a cumulative variance contribution rate of 59.08%. Confirmatory factor analysis validates the model’s fit (χ²/df = 4.424, RMR = 0.021, RMSEA = 0.079, SRMR = 0.023, IFI = 0.920, TFI = 0.916, CFI = 0.920, PNFI = 0.855, and PCFI = 0.875). All dimensions demonstrate satisfactory convergent validity, and discriminant validity is also good.

Date collection

The Chinese-language questionnaire, generated in an electronic format using the Sojump software, was disseminated via QR codes and links on social media platforms like WeChat and QQ for an online survey from July to October 2023. Participants were made aware of the aims, significance, inclusion and exclusion criteria and the confidentiality of their personal information before starting. After giving permission, they could finish the questionnaire based on their individual situations. To ensure the quality of the survey, several safeguards were put in place. In particular, the questionnaire was designed to prevent repetitive submissions from the same device. Moreover, all items needed to be answered before submission. After collecting all surveys, a rigorous screening process was undertaken to recognize potential problems, such as unusually short completion times (less than 240 s) or consistent responses across all items. This time limit of four minutes was determined by our research group based on the estimated minimum time required to read through all questions at least once.

Data analysis

Statistical analyses were conducted using SPSS 26.0 (IBM, NY, USA). Categorical data were described using frequencies and percentages. As continuous variable data exhibited normal distribution, they were described using mean and standard deviation. Furthermore, we explored associations between certain characteristics and nurses’ total scores on the EBHC instrument using the independent samples t-test, Adjusted t-test, one-way Analysis of Variance (ANOVA), Welch ANOVA, or Pearson’s correlation test. The test selection was based on both the type of the independent variables (continuous, binary, or multi-categorical) and the results of homogeneity of variance test. Continuous variables were analyzed using Pearson’s correlation test; binary variables with either independent samples t-test (The data met the variance homogeneity criterion, P > 0.05) or adjusted t-test (The data did not meet the variance homogeneity criterion, P < 0.05); and multi-categorical variables with either one-way ANOVA (The data met the variance homogeneity criterion, P > 0.05) or Welch ANOVA (The data did not meet the variance homogeneity criterion, P < 0.05). Finally, we performed multiple linear regression analysis to examine the effect of the independent variables on the total scores of the nurses’ EBHC competence. To validate the multiple linear regression assumptions, we assessed multicollinearity using variance inflation factors (VIF < 5), examined standardized residual plots with P-P plots to verify normality, and conducted Durbin-Watson tests to confirm absence of autocorrelation (DW statistic ranges 0–4, with values approaching 2 indicating no autocorrelation.). The bilateral threshold for significance was set at a value of p ≤ 0.05, and the confidence interval was set at 95% (95% CI).

Results

Characteristics of participants

A total of 720 questionnaires were collected for this study. After excluding those with completion time less than four minutes and questionnaires with identical answers to all items on the instrument, 531 valid questionnaires remained, representing a valid response rate of 73.75%. The sample consisted of 531 clinical nurses aged between 21 and 55 years, with an average age of (33.72 ± 6.54) years. Of these, 22 were male (4.1%), and 509 were female (95.9%). Most nurses (93.8%, n = 498) worked in tertiary hospitals, while the remaining 6.2% (n = 33) worked in secondary hospitals. In terms of educational background, 6.6% (n = 35) had a diploma degree, 90.2% (n = 479) had a bachelor’s degree, and 3.2% (n = 17) had a master’s degree. Regarding their professional titles, 34.7% (n = 184) were at the junior level, 58.8% (n = 312) were intermediate, 6.0% (n = 32) were associate senior, and 0.6% (n = 3) were senior, as detailed in Table 1.

Table 1.

Demographic and professional characteristic of participants (n = 531)

Characteristics	n (%)
Gender
Male	22(4.1)
Female	509(95.9)
Age	33.72 ± 6.54
Marital Status
Married	391(73.6)
single	140(26.4)
Number of children
None	176(33.1)
One	164(30.9)
Two or more	191(36.0)
Educational background
Diploma	35(6.6)
Bachelor’s degree	479(90.2)
Master’s degree	17(3.2)
Hospital grade
Secondary	33(6.2)
Tertiary	498(93.8)
Department
Internal medicine department	147(27.7)
Surgical department	138(26)
Gynaecology and obstetrics department	38(7.2)
Pediatric department	23(4.3)
Emergency department	16(3)
Intensive care unit	27(5.1)
Others	142(26.7)
Working years	11.95 ± 7.23
Professional title
Junior title	184(34.7)
Intermediate title	312(58.8)
Associate senior title	32(6.0)
Senior title	3(0.6)
Workload
Very busy	173(32.6)
Busy	321(60.5)
Moderately busy	37(7.0)
Not busy	0(0.0)
Very unbusy	0(0.0)
Job satisfaction
Very dissatisfied	5(0.9)
Dissatisfied	54(10.2)
Moderately satisfied	190(35.8)
Satisfied	258(48.6)
Very satisfied	24(4.5)
English literature reading ability
Very poor	121(22.8)
Poor	217(40.9)
Neutral	170(32.0)
Good	23(4.3)
Very good	0(0.0)
The frequency of literature reading
Never	31(5.8)
Occasionally	250(47.1)
Sometimes	182(34.3)
Often	61(11.5)
Always	7(1.3)
Whether you have published articles in public journals
Yes	210(39.5)
No	321(60.5)
Whether you have applied for research projects
Yes	78(14.7)
No	453(85.3)
Whether you have applied for patents
Yes	85(16.0)
No	446(84.0)
The number of types of evidence-based training programs you participated in
None	188(35.4)
One type	164(30.9)
Two types	67(12.6)
Three types	78(14.7)
Four types	31(5.8)
Five types	3(0.6)
Whether you have participated in evidence implementation programs
Yes	98(18.5)
No	433(81.5)

Note: Data are n (%), unless otherwise indicated

The scores of EBHC instrument

Among the 531 nurses, the total score of EBHC competence was (137.05 ± 36.84) points, with an average score per item of (2.92 ± 0.78) points. The mean scores of each dimension were as follows: evidence generation (2.83 ± 0.87) points, evidence synthesis (2.87 ± 0.83) points, evidence transfer (3.05 ± 0.85) points, and evidence implementation (3.01 ± 0.83) points. Details are shown in Table 2.

Table 2.

Mean scores and standard deviations of each item of EBHC instrument (n = 531)

Item	Score (Mean ± SD)	Ranking
Evidence generation	2.83 ± 0.87
I am familiar with one or more types of quantitative research designs (e.g., RCT, Cohort Study, Case-Control Study, Analytical Cross-Sectional Study, and/or Prevalence Study).	2.84 ± 0.99	7
I am familiar with one or more types of qualitative research designs (e.g., Phenomenology, Grounded Theory, Ethnography, Interpretive Description, Narrative Analysis, Thematic Analysis, and/or Action Research).	2.66 ± 1.00	17
I am able to choose appropriate theoretical framework(s) to guide the research when required.	2.84 ± 0.99	7
I am able to set up selection criteria to recruit study participants pertinent to the study aim(s).	3.00 ± 1.02	2
I am able to choose an appropriate sampling method.	2.93 ± 0.97	4
I am able to choose an appropriate method to calculate a sample size.	2.82 ± 0.99	10
I am able to develop intervention(s) in an experimental study.	2.75 ± 0.99	14
I am able to choose relevant measurement indicators to reflect the study outcomes upon the study aim(s).	2.78 ± 1.00	11
I am able to collect research data using appropriate methods (e.g., observation, interview, and survey).	3.03 ± 1.01	1
I am able to control bias by suitable methods.	2.76 ± 0.98	13
I am able to apply appropriate statistical software and employ proper statistical methods to analyze quantitative data.	2.74 ± 1.00	15
I am able to interpret research results correctly and draw inference(s).	2.84 ± 0.98	7
I am able to develop interview questions pertinent to the purpose(s) of a qualitative study.	2.77 ± 1.00	12
I am able to choose appropriate methods to manage qualitative data for analysis (manually or using a software).	2.73 ± 0.99	16
I am able to take ethical issues into account when design a study through applying for an ethical approval.	2.96 ± 1.05	3
I am able to register a study on a widely recognized platform when required.	2.85 ± 1.04	6
I am able to develop a written research report(s) for dissemination following a relevant reporting guideline.	2.87 ± 1.00	5
Evidence synthesis	2.87 ± 0.83
I am able to develop a structured systematic review question following the framework of PICO, PICo, PCC, etc.	2.77 ± 1.01	10
I am able to set up appropriate inclusion and exclusion criteria for the selection of relevant literature.	2.88 ± 1.00	4
I am able to develop appropriate searching strategies to systematically retrieve literature (e.g., the types of evidence to search, search terms, search databases, and search time).	3.08 ± 0.98	3
I am able to systematically retrieve literature from domestic and international databases (e.g., CNKI, WanFang, VIP, PubMed, CINAHL, MEDLINE, and Web of Science, UpToDate, BMJ Best Practice, Cochrane Library, and Medlive), and gray literature (e.g., Google Scholar and registered website).	3.15 ± 0.99	1
I am able to use literature management software to manage literature (e.g., Endnote, Note Express, and Zotero).	2.85 ± 1.03	5
I am able to choose appropriate critical appraisal tool(s) to assess the quality of literature according to the type of the literature (e.g., JBI’s critical appraise tools, AMSTAR-2, and AGREE II).	2.74 ± 0.98	11
I am able to extract data from the literature included in a review.	3.10 ± 0.98	2
I am able to grade the quality of evidence extracted from included literature based on an evidence-grading system (e.g., GRADE).	2.82 ± 0.96	6
I am able to synthesize quantitative or qualitative data.	2.80 ± 0.96	7
I am able to develop a written systematic review protocol following a reporting guideline and register it on a widely recognized platform.	2.73 ± 0.98	12
I am able to develop a written systematic review report following a reporting guideline.	2.79 ± 0.96	8
I am able to develop an evidence summary.	2.78 ± 1.00	9
Evidence transfer	3.05 ± 0.85
I often share best available evidence with others.	3.14 ± 0.95	1
I am able to disseminate best available evidence through various ways such as web platform, best practice information booklet, PPTs, evidence summary report, decision-support tool, presentations, or media.	3.09 ± 0.96	2
I am devoted to building an atmosphere conducive to evidence-based practice in the workplace.	3.07 ± 0.93	3
I am able to apply my leadership to promote evidence-based practice in healthcare settings.	2.92 ± 0.92	4
Evidence implementation	3.01 ± 0.83
I am able to identify evidence-practice gap(s) in practical settings.	2.92 ± 0.92	11
I have prepared to apply best available evidence to guide my clinical decisions in daily work.	3.14 ± 0.96	4
I am able to identify an evidence implementation project topic.	2.97 ± 0.94	9
I am able to apply theories/models/frameworks to guide the conduct of evidence implementation project(s).	2.88 ± 0.96	13
I am able to convert evidence to actionable work procedures.	2.99 ± 0.97	6
I am able to perform a context analysis of environment where evidence is to be implemented.	3.00 ± 0.98	5
I am able to identify barriers and facilitators to evidence implementation.	2.99 ± 0.95	6
I am able to fight for or mobilize resources (e.g., human, financial, physical, and technological resources) to support evidence implementation.	2.86 ± 0.99	14
I am able to develop solutions to overcome the key barriers to evidence implementation.	2.91 ± 0.94	12
I am able to monitor the process of evidence implementation.	2.95 ± 0.95	10
I am able to evaluate the effect of evidence implementation via applying appropriate outcome indicators.	2.99 ± 0.96	6
I am able to seek feedback from stakeholders during the evidence implementation.	3.23 ± 1.02	1
I am able to reflect on the issues of evidence implementation and find solutions.	3.16 ± 0.95	3
I am able to actively take measures to reinforce the continuous implementation of evidence.	3.19 ± 0.96	2

Relationship between characteristics and the total scores of EBHC instrument

Statistically significant differences were observed in the total scores of EBHC competence among clinical nurses with varying characteristics. For instance, nurses with a master’s degree scored higher in EBHC than those with a bachelor’s or diploma degree (F = 35.973, P < 0.001). Additionally, nurses with a busy or moderately busy daily workload tended to achieve higher scores in EBHC assessments compared to those with a very busy workload (F = 4.599, P = 0.012). Moreover, the EBHC score increased significantly as nurses’ ability to read English literature improved (F = 30.746, P < 0.001). Similarly, EBHC scores were significantly higher among nurses who often or always read literature compared to others (F = 10.370, P < 0.001). Furthermore, the EBHC score increased significantly as the number of types of evidence-based training programs nurses participated in increased (F = 5.848, P < 0.001). Figure 1 further illustrates score variations across individual items among nurses grouped by the number of training program types participated in. Finally, EBHC scores were notably higher among nurses who had published articles in public journals (t = 3.946, P < 0.001), applied for research projects (t = 3.330, P = 0.001), applied for patents (t = 3.839, P < 0.001), and participated in evidence implementation programs (t = 2.667, P = 0.008). However, no significant difference was found in terms of gender, age, marital status, number of children, hospital grade, department, working years, professional title, and job satisfaction (Table 3).

Fig. 1.

EBHC competence scores by number of types of EB training program. EB training program types: Six categories (scientific research, systematic review, guideline, evidence summary, evidence transfer, and evidence implementation training). Training participation groups: None (no training), One type (completed any single training category), …, Five types. The “All” line shows combined total scores from all nurses (None/One type/…/Five types groups). EBHC dimensions: Items 1–17 (evidence generation), 18–29 (evidence synthesis), 30–33 (evidence transfer), 34–47 (evidence implementation)

Table 3.

Relationship between nurses’ characteristics and their total scores on the EBHC competence (n = 531)

Characteristics	EBHC competence	t/F/r-value	P-value
Educational background		38.870d	0.000
Diploma	147.80 ± 28.57
Bachelor’s degree	134.80 ± 36.90
Master’s degree	178.12 ± 19.17
Hospital grade		1.733a	0.084
Secondary	147.79 ± 41.81
Tertiary	136.34 ± 36.43
Department		0.654c	0.687
Internal medicine department	135.48 ± 35.44
Surgical department	135.38 ± 40.23
Gynaecology and obstetrics department	145.79 ± 34.02
Pediatric department	139.30 ± 29.55
Emergency department	139.81 ± 30.89
Intensive care unit	130.52 ± 30.39
Others	138.51 ± 38.47
Working years		-0.043b	0.322
Professional title		1.712c	0.163
Junior title	138.91 ± 36.91
Intermediate title	134.67 ± 37.11
Associate senior title	148.22 ± 32.77
Senior title	150.67 ± 28.10
Workload		4.599d	0.012
Very busy	130.23 ± 39.50
Busy	139.68 ± 35.02
Moderately busy	146.08 ± 35.52
Job satisfaction		2.290c	0.059
Very dissatisfied	106.40 ± 35.85
Dissatisfied	137.13 ± 41.65
Moderately satisfied	132.50 ± 36.52
Satisfied	140.51 ± 36.02
Very satisfied	142.04 ± 32.62
English literature reading ability		30.746d	P<0.001
Very poor	115.93 ± 45.90
Poor	135.03 ± 31.04
Neutral	150.71 ± 28.96
Good	166.22 ± 21.90
The frequency of literature reading		10.370d	P<0.001
Never	108.39 ± 42.11
Occasionally	133.03 ± 36.31
Sometimes	141.85 ± 35.71
Often	150.30 ± 29.77
Always	167.29 ± 23.06
Whether you have published articles in public journals		3.946e	P<0.001
Yes	144.60 ± 34.26
No	132.11 ± 37.68
Whether you have applied for research projects		3.330a	0.001
Yes	149.76 ± 31.76
No	134.86 ± 37.24
Whether you have applied for patents		3.839e	P<0.001
Yes	148.80 ± 29.29
No	134.81 ± 37.73
The number of types of evidence-based training programs you participated in		5.848c	P<0.001
None	130.23 ± 37.25
One type	134.09 ± 37.01
Two types	137.46 ± 35.45
Three types	153.72 ± 32.76
Four types	148.74 ± 31.07
Five types	162.00 ± 45.74
Whether you have participated in evidence implementation programs
Yes	145.96 ± 34.76	2.667a	0.008
No	135.03 ± 37.04

Note: a represents independent samples t-test, b represents Pearson’s correlation test, c represents one-way ANOVA, d represents Welch ANOVA, e represents adjusted t-test

Multiple linear regression analysis for total score of EBHC competence

To determine which individual characteristics independently influence clinical nurses’ EBHC competence, we conducted a multiple linear regression analysis. The results revealed that English literature reading ability (β = 0.306, CI: 9.897 to 17.223), the frequency of literature reading (β = 0.125, CI: 1.894 to 9.364), workload (β = 0.113, CI: 2.238 to 12.187), the number of types of evidence-based training programs nurses participated (β = 0.083, CI: 0.005 to 4.814), and whether they had applied for patents (β = 0.101, CI: 2.356 to 17.986) significantly influenced the EBHC competence (P<0.05). Detailed information can be found in Table 4.

Table 4.

Multiple linear regression analysis for total score of EBHC competence (n = 531)

Model	B-value	SE-value	β-value	t-value	P-value	VIF	95% CI
(constant)	75.877	6.798	-	11.161	<0.001	-	62.522 ~ 89.233
English literature reading ability	13.560	1.865	0.306	7.272	<0.001	1.154	9.897 ~ 17.223
the frequency of literature reading	5.629	1.901	0.125	2.960	0.003	1.172	1.894 ~ 9.364
Workload	7.212	2.532	0.113	2.848	0.005	1.019	2.238 ~ 12.187
The number of types of evidence-based training programs you participated in	2.409	1.224	0.083	1.968	0.050a	1.165	0.005 ~ 4.814
Whether you have applied for patents	10.171	3.978	0.101	2.557	0.011	1.025	2.356 ~ 17.986

Note: R = 0.442, R Square = 0.196, Adjusted R Square = 0.188, F = 25.540, P<0.001. a represents P-value = 0.04954<0.050, DW = 2.206 (Durbin-Watson statistic)

Discussion

The aim of this study was to assess systematically and comprehensively the EBHC competence of clinical nurses and explore the associated factors. Although previous studies have examined the EBHC competence of clinical nurses, their scope has often been restricted to specific, isolated phases of the EBHC process, and assessments may also lack granularity in capturing the full spectrum of required competencies. This study systematically investigated the competence across all phases of EBHC according to the JBI EBHC Model, encompassing evidence generation, synthesis, transfer, and implementation, from a task-performance perspective to measure the endpoint, while identifying associated factors of this competence.

The results of this study indicated that the clinical nurses surveyed perceived themselves to have a moderate level of EBHC competence, while also reporting discrepancies in competence levels both among and within individual EBHC phases. Notably, the areas where they demonstrated higher levels of competence were in transferring and implementing evidence, whereas their lower areas were generating and synthesizing evidence. This finding was consistent with previous studies conducted in Finland, Africa, and Iran [8, 9, 18, 23]. One possible reason for the higher competence in transferring and implementing evidence is that these activities are often more integrated into daily clinical practice. Nurses may have more opportunities to engage directly with patients and utilize evidence, which can lead to greater familiarity and confidence in these areas. Additionally, many healthcare organizations establish evidence implementation bases to provide healthcare professionals with training and resources for implementing evidence [24, 25], thereby motivating nurses to engage in evidence-based practice and enhancing their competence to translate research findings into practical care settings. In contrast, the lower competence in generating and synthesizing evidence might be due to the fact that these skills require a deeper understanding of research methodology and statistical analysis, which may not be as routinely practiced by clinical nurses. Moreover, the time and resources required to conduct original research or evidence synthesis can be significant, and such activities may not always be prioritized in the busy and resource-constrained environment of clinical practice. These factors may contribute to the observed discrepancy in competence levels across the different phases of EBHC. From the EBHC ecosystem perspective, it should be acknowledged that evidence generation is the foundation of evidence synthesis, which in turn underpins evidence implementation. Hence, organizations should give equal attention to improve the evidence generation and synthesis competencies of clinical nurses.

As for specific competence criteria, clinical nurses recognized that they possessed relatively stronger competencies in areas such as sharing evidence with others [18, 23, 26], collecting research data [23], conducting systematic literature searches [12], and seeking feedback from stakeholders, compared to other aspects of their work. Conversely, they identified weaknesses in their competencies, particularly in the domains of leadership skills [12], qualitative research studies, developing systematic review protocol, appraising literature critically [12, 18, 23, 26], and securing or mobilizing resources essential for the implementation of evidence-based practices [27]. These findings highlighted the need for targeted interventions to address these EBHC competence gaps.

The results of this study showed a positive association between English literature reading ability and nurses’ EBHC competence. Previous research conducted in East Iran, a non-native English-speaking region, indicated that proficiency in reading English-language literature is crucial for understanding the latest international original research, systematic reviews, guidelines, and evidence summaries, as well as updating evidence-based methodologies [18]. In the current academic landscape where English is the predominant language for scientific communication, nurses who are non-native English users with strong English reading skills are better equipped to access and understand a wide range of international literature. Furthermore, avccording to Bandura’s Social Cognitive Theory [28], observational learning occurs through four sequential processes: attention, retention, reproduction, and motivation. Reading relevant literature is essentially a form of observational learning. Proficient English literature reading ability facilitates observational learning by enabling nurses to effectively access and learn from international evidence sources. Through this process, individuals can gain insights into the various stages of the EBHC process, such as conducting research, synthesizing evidence, and applying current best evidence, thereby developing a comprehensive understanding of the entire process. Language barriers disproportionately impair the initial attention phase, as limited comprehension reduces observable exemplars. Thus, healthcare organizations should provide structured English language training programs specifically tailored for nurses. These programs can incorporate various strategies such as vocabulary building, interactive workshops, and peer mentoring.

Our findings revealed that the frequency of literature reading was significantly associated with nurses’ EBHC competence. This is consistent with previous studies [11, 13, 29]. Nurses who read literature frequently are more likely to stay abreast of latest and best evidence, according to the Conservation of Resources Theory [30], individuals are motivated to acquire, retain, and defend their resources. In this study, frequent engagement with literature serves as a valuable resource that supports professional development and maintains competencies. Therefore, healthcare organizations can improve their data resource systems to facilitate access to literature and organize regular reading activities to foster a reading habit, thereby supporting nurses in enhancing their EBHC competence.

We found that the level of workload experienced by nurses was correlated with their EBHC competence. This finding was in line with prior research indicating that high workload can impede nurses’ ability to engage in the EBHC process [20, 31]. According to the Threat Rigidity Theory, when individuals perceive a threat, they experience a rigidity effect manifested in restriction in information processing. Such restriction primarily involves relying on pre-existing knowledge, limiting diverse information processing, reducing information sharing, narrowing the range of behavioral choices, and favoring routine responses, leading to conservative behavior [32]. Conversely, when perceiving opportunities, individuals increase their intrinsic motivation, actively participate in facing challenges, enhance their self-efficacy, and proactively learn [32]. In this study, heavy workload serves as a perceived threat for clinical nurses, prompting them to adopt conservative behaviors, such as relying on experience for clinical decision-making. Consequently, the likelihood of generating, synthesizing, and transferring, implementing evidence after identifying clinical issues decreases, leading to a stagnation in their EBHC competencies. To mitigate this issue, healthcare organizations should consider strategies such as optimizing staffing levels and incorporating technology to streamline processes, thereby alleviating the workload.

It was observed that the experience of having applied for patents showed a significant association with nurses’ EBHC competence.These finding is consistent with a previous study [10]. One plausible explanation for this phenomenon is that nurses with patent application experience tend to be more proactive and insightful in recognizing problems. They possess the ability to address these issues by inventing related methodologies and developing products, and the whole process actually embodies evidence-based thinking. Reflecting in their clinical work, it is easier for them to identify problems and assist themselves in making clinical decisions by generating, synthesizing, or implementing evidence. It is recommended that healthcare administrators refine incentive mechanisms to encourage nurses’ active participation in patent application.

The study identified a significant association between the number of types of evidence-based training programs (e.g., systematic review training, evidence summary training, guideline training, evidence implementation training) in which nurses participated and their EBHC competence. However, the study indicated that nurses’ level of education was not associated with their EBHC competence. For one, this finding supports previous studies emphasizing the importance of diverse training experiences [13, 29, 33]. For another, this finding is inconsistent with previous studies that have indicated a positive association between nurses’ level of education and their EBHC competence [9, 11, 34]. Regarding this finding, first, our sample may have included clinical nurses with a more homogeneous educational background, reducing the variability needed to detect a significant association. Second, the context of practice and the availability of continuing, intensive, and systematic training opportunities might have played a significant role in shaping nurses’ EBHC competence [35–37].

Nowadays, many evidence-based continuing training programs utilize a dedicated period of time focused on a specific topic, making them more targeted and competency-oriented. For instance, the systematic review training organized by the Joanna Briggs Institute (JBI) and the guideline development training organized by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) center. Clinical nurses participating in these training programs are primarily motivated by clinical needs and often identify specific clinical issues requiring evidence synthesis, generation, or implementation. Consequently, they enroll in training programs that correspond to these specific topics. Upon completion, they are able to apply what they have learned directly in their work, which not only aids in knowledge retention but also contributes to the development of competencies.

In contrast, the lack of association between nurses’ level of education and their EBHC competence in our study highlights potential limitations within some traditional educational approaches. While higher levels of education undoubtedly provide foundational knowledge, these lessons may not always effectively translate theoretical knowledge into competencies without complementary hands-on training. This finding indicates that current educational programs might place a greater emphasis on knowledge acquisition rather than being competency-oriented, which can result in a mismatch with the practical demands of the workplace. Competency-Based Education considers competencies as the ultimate outcomes guiding curriculum development, including implementation, assessment, and evaluation [38, 39]. Recent studies highlight the need for competency-based education in healthcare, emphasizing alignment with practical needs [38, 40]. The competency-based approach is worth adopting for the development of EBHC competence among clinical nurses. In particular, degree education needs to evolve from traditional models toward a competency-based approach, where course design and evaluation focus on developing competencies that align with real-world requirements [41, 42]. evidence-based continuing training programs should also adhere to the principle of being competence-oriented.

This study has several strengths. Firstly, the instrument used to measure EBHC competence encompasses all phases of the EBHC process, and each dimension includes remarkably specific criteria, allowing for a systematic and effective evaluation of nurses’ competencies. This enables nurses to recognize their strengths and weaknesses and provides concrete direction for improvement. Furthermore, healthcare organizations can make targeted interventions based on the specific areas for improvement identified. However, several limitations need to be addressed in future studies. For one thing, a convenience sample was used, which may have led to sampling bias and limited the generalizability of the findings. For another, we relied on a self-reported instrument to measure EBHC competence, which may be subject to response biases. Lastly, while the study identified several factors influencing EBHC competence, a more in-depth exploration of these factors is needed.

Conclusion

The cross-sectional study identified the EBHC competencies of clinical nurses and associated factors. It is believed that the findings of this research endeavor can furnish healthcare organizations with valuable insights to devise targeted interventions aimed at bridging the identified gaps in EBHC competence among clinical nurses. By implementing such interventions, healthcare organizations can effectively augment the EBHC competencies of their nursing staff, thereby ensuring the delivery of high-quality care and ultimately contributing to the advancement of global health outcomes.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

NW, SW and JL conceived the manuscript concept and analysis of data; all authors have contributed to the acquisition of data; SW and JL undertook the initial drafting of the manuscript and statistical analysis; NW and SW undertook critical revision of the manuscript for important intellectual content. LLZ provided administrative, technical, or material support.

Funding

This study was supported by the Guangdong Province Teaching Quality and Teaching Reform Project for 2021 (JG2021113) and Guangdong Province Clinical Teaching Base Reform Research Project for 2023. These funding sources had no role in the design of this study and did not have any role during its execution, analyses, interpretation of the data or decision to submit results.

Data availability

The data presented in this study are available upon reasonable request from the corresponding author.

Declarations

Ethics approval and consent to participate

This study was carried out in accordance with the principles outlined in the updated Declaration of Helsinki. This study involved human participants and was approved by the Ethical Review Committee of The Seventh Affiliated Hospital, Southern Medical University (Number: (2022)-0020) prior to data collection. Informed consent was obtained from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.