Adaptation and validation of Chinese version of the creativity and innovation effectiveness profile

Abstract

Aim

This study aimed to adapt and validate the Creativity and Innovation Effectiveness Profile (CIEP) for Chinese nursing students, developing a culturally contextualized tool to systematically assess and enhance creativity in alignment with China’s educational and cultural contexts.

Background

Given the rapid evolution of global healthcare driven by advances in technology, clinical practice, and patient care models, fostering innovation in nursing education has become imperative. While China has demonstrated a growing emphasis on cultivating nursing students’ innovative capabilities, there remains a persistent lack of validated instruments specifically designed to evaluate these competencies within this population.

Design

A scale adaptation and validation study.

Methods

The cross-cultural adaptation process involved forward-backward translation, expert reviews (using the Delphi method with 15 experts), cognitive interviews (n = 30), and psychometric validation. Data from 531 nursing students from two universities were analyzed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

Results

The adapted scale was restructured into four dimensions—Creative Consciousness, Pattern Breaking Skills, Critical Thinking Ability, and Idea Nurturing Ability. Results demonstrated high internal consistency (Cronbach’s α = 0.894), excellent test-retest reliability (ICC = 0.846), and strong construct validity (CFI = 0.925, RMSEA = 0.051). Composite reliability (0.862–0.911) and discriminant validity further confirmed the scale’s psychometric rigor. Culturally sensitive modifications enhanced the scale’s ecological validity.

Conclusion

The validated 31-item CIEP provides a systematic tool for evaluating and fostering innovation competencies within Chinese nursing education, thereby supporting national priorities for multidisciplinary healthcare talent development. Future research should focus on longitudinal tracking of innovation capability development and establishing empirical correlations between scale-derived indicators and objective clinical innovation outcomes.

Introduction

Innovative capacity of nursing students refers to their ability to creatively identify and solve problems during academic and clinical training by leveraging their creative personalities through relevant thinking and knowledge application [1]. As global healthcare continues to evolve,rapidly, with continuous advancements in technology, clinical practices, and patient care models, cultivating creativity and innovation in nursing education has become increasingly essential.

Recognizing this imperative, China has placed greater emphasis on cultivating innovative capabilities in nursing students. The Guiding Opinions of The General Office of the State Council on Accelerating the Innovative Development of Medical Education specifically highlights the need for training innovative talents with multidisciplinary backgrounds, particularly in “Medicine + X” fields, to better prepare nursing students for global healthcare challenges [2]. However, despite this policy-driven demand, a critical gap persists: no validated assessment tool exists to measure innovation capabilities specifically among Chinese nursing students.

While the importance of creativity in nursing practice is widely acknowledged, developing appropriate measurement tools presents significant challenges. Existing instruments demonstrate notable limitations for this population. The Torrance Tests of Creative Thinking (TTCT) were primarily designed for younger students in primary and secondary education, focusing on assessing innovative potential rather than practical application in professional contexts [3]. Similarly, the Williams Creativity Scale (WCS) exhibits limited cultural adaptability and professional specificity for nursing practice [4]. In contrast, the Creativity and Innovation Effectiveness Profile (CIEP) developed by Jon Warner [5] offers greater promise, as it assesses eight dimensions directly relevant to professional innovation(e.g.,vision orientation, environmental responsiveness, task focus, risk tolerance, thinking diversity, and problem-solving flexibility), which closely aligns with nursing competency requirements. However, the CIEPs Western cultural framework may conflict with China’s collectivist values and standardized clinical guidelines, potentially limiting its direct application in Chinese contexts.

This measurement gap has significant implications for nursing education research in China.Currently, Chinese researchers frequently rely on self-developed questionnaires that lack rigorous psychometric evaluation, thereby compromising research validity [6]. To address these limitations, this study aims to adapt and validate the CIEP for Chinese nursing students, creating a culturally contextualized instrument that provides a validated method to systematically assess and cultivate nursing students’ creativity in a manner compatible with Chinese educational and cultural contexts.

Methods

Study design

In order to thoroughly translate, culturally adjust, and assess the validity and reliability of the Chinese version of the Creativity and Innovation Effectiveness Profile (CIEP), this study used cross-cultural adaptation.

Setting and participants

Students from the nursing departments of two public colleges in Beijing and Hebei Province participated in the convenience sample study, which ran from January 20 to February 28, 2025. This is mostly predicated on taking into account the real research environment and available resources. In particular, sample acquisition relies on the teams that researchers have at their disposal to effectively gather fundamental data with constrained resources, including time, money, and personnel. Students pursuing a bachelor’s degree in nursing from all grade levels met the inclusion requirements. The following were the exclusion criteria:

(1) Undergraduate students not majoring in nursing;(2) Those who refused to participate in the study or failed to sign the informed consent form;(3) Individuals with insufficient Chinese proficiency to complete the questionnaire independently;(4) Questionnaires with missing key items exceeding 10% or showing obvious patterns of random responses;(5) Participants currently receiving psychiatric treatment, which may affect the accuracy of scale measurements. To determine the sample size of the study, the number of scale items was taken as a basis, which is a widely accepted method in scale validity and reliability studies. After cultural adaptation, the final scale has 31 items.The minimal sample size needed was 155 (31 × 5), while the optimal size was 310 (31 × 10), in accordance with psychometric standards that suggest a participant-to-item ratio of at least 5:1 to 10:1. The ideal threshold was exceeded by our sample of 531 people [7, 8].

Measures

Data collection was conducted using electronic questionnaires, which included a personal information form and the Creativity and Innovation Effectiveness Profile(CIEP).

Personal information form

The form was designed by the researchers, covering gender, grade level, participation in innovation competitions, and other relevant demographic information.

Chinese version of creativity and innovation effectiveness Profile (CIEP)

We employed the Creativity and Innovation Effectiveness Profile (CIEP), which was created by American researcher Jon Warner in 2004, to evaluate the present state of creativity and innovation skills among undergraduate nursing students [5]. The original scale includes 7 dimensions: Creative consciousness, Levels of curiosity, Pattern breaking skills, Idea nurturing ability, Willingness to experiment and take risks, Courage and resilience levels, and Energetic persistence. Each dimension contains 12 items. A Likert 5-point scale is used for scoring, with the following options: 1 = Almost Never, 2 = Occasionally, 3 = Frequently, 4 = Very Frequently, 5 = Almost Always. The higher the score, the higher the level of innovative ability.

The procedure of translation and testing validity and reliability of the CIEP

In accordance with accepted standards for the cross-cultural adaptation and validation of psychological tools, this study used a multi-phase technique.Getting approval from the Creativity and Innovation Effectiveness Profile’s original creator was the first step. The following phases made up a methodical cross-cultural adaption procedure.

Forward translation and synthesis

To ensure the applicability of the scale in a Chinese language environment, we employed the standard translation-back translation method with enhanced quality control measures to ensure semantic equivalence. First, two independent bilingual translators proficient in both the source English and Chinese languages and with backgrounds in psychology and nursing were invited to translate the original scale into the target language independently and in parallel. Both translators were provided with detailed instructions regarding the theoretical framework of the instrument and the target population to ensure conceptual understanding.

To systematically address translation discrepancies and ensure semantic equivalence, the research team established a comprehensive synthesis protocol. Following translation, the research team conducted a synthesis meeting involving the research team members, translation experts, and domain specialists to resolve any discrepancies between the translations and produce a consensus version. During this process, each item was examined for conceptual equivalence (whether the same concept is conveyed), semantic equivalence (whether the same meaning is expressed), and operational equivalence (whether the same response pattern is elicited). Discrepancies were documented, discussed, and resolved through consensus, with detailed rationales recorded for each decision.

The translated scale was then retranslated into English by two more fluent Chinese translators who were oblivious to the original English version. To find semantic differences between the back-translated version and the original English scale,a methodical comparison procedure was used.Major disparities necessitated an immediate rewriting of the Chinese version,whereas minor discrepancies were classified as word choice variants with equivalent meaning.

Expert review

This study invited 15 experts in nursing education and clinical nursing to form an expert panel to conduct a cultural adaptation assessment of the original scale. The expert panel includes scholars in nursing education, clinical nursing experts, and psychometricians, all of whom have relevant research and clinical experience.

To address translation discrepancies and ensure semantic equivalence, a two-round Delphi method was used for expert consultation. In the first round, experts were presented with both the original English items and the Chinese translations, along with documentation of any discrepancies identified during the back-translation process. Experts independently evaluated the content validity, cultural relevance, semantic equivalence, and language expression of each item, scoring them using a 4-point Likert scale (1 = not relevant, 4 = highly relevant) with an additional 4-point scale for semantic equivalence assessment (1 = poor equivalence, 4 = excellent equivalence).

Specific criteria for semantic equivalence evaluation were provided to experts, including: (1) conceptual equivalence - whether the Chinese version captures the same underlying concept as the English version; (2) semantic equivalence - whether the meaning is preserved across languages; and (3) operational equivalence - whether the item would elicit similar response patterns in both cultures. Items scoring below 3.0 on semantic equivalence were flagged for revision.

The item-level content validity index (I-CVI) and the scale-level content validity index (S-CVI) were calculated based on expert ratings. Items with an I-CVI below 0.78 or semantic equivalence scores below 3.0 were modified or deleted. In the second round, experts reviewed the revised items and confirmed that semantic equivalence had been achieved. Qualitative feedback from experts was analyzed to determine the dimensions and items that needed adjustment.

Preliminary survey

Researchers distributed questionnaires to nursing students across all academic years at the two universities.A pilot study was conducted with 30 participants to assess the feasibility of the research protocol, clarity of items, and preliminary psychometric properties of the scale.

Cognitive interviewing

Semi-structured interviews with the same 30 undergraduate nursing students were conducted to assess item comprehensibility and cultural appropriateness, with particular attention to semantic equivalence validation. revising ambiguous items based on feedback to ensure optimal semantic equivalence. The interviews followed a think-aloud protocol, where participants verbalized their thought processes while responding to each item. Specific probes were used to assess whether participants’ interpretation of items aligned with the intended meaning of the original English version, thereby validating semantic equivalence at the individual level. Any items showing consistent misinterpretation or alternative meanings were flagged for further revision.

Data collection

The research team collected data from January 20, until February 28.The data was collected online.The researchers personally conducted unified online training for those responsible for data collection to ensure the scientific rigor of the data collection process. All questions were designated as mandatory, and the survey was configured so that each IP address could submit only once. Only students who met the inclusion criteria and provided informed consent were allowed to participate in the astudy.Students could begin answering after scanning the QR code, and questionnaires with answers showing obvious consistency patterns indicative of random or inattentive responding were excluded.It took approximately 20–30 minutes to fill out the questionnaire.

Data analysis

SPSS version 27.0 (IBM, Armonk, NY, USA) was utilized to analyze the data, and descriptive statistics like frequency and percentage were employed to examine the demographic traits of the pupils. One of the researchers thoroughly examined the raw data to ensure its accuracy and integrity before beginning the study.

Cronbach’s alpha coefficient was used to measure the internal consistency reliability [9]. The intraclass correlation coefficient (ICC) was calculated to determine the test-retest reliability [10]. The factor structure of the Chinese version of Creativity and Innovation Effectiveness Profile was evaluated by conducting an Exploratory Factor Analysis (EFA). We assessed sampling adequacy using the Kaiser-Meyer-Olkin (KMO) value and the Bartlett test of sphericity [9]. The Principal Component Analysis (PCA) extraction with Promax rotation methods was used in the EFA. To determine the appropriate number of factors, eigenvalues had to be > 1.0 [11]. It was proposed that 0.40 would be the most widely utilized limit for factor loading [12]. We used a two-sample approach for validation: the Beijing University sample was used for EFA, while the Hebei Province University sample was used for Confirmatory Factor Analysis (CFA).Confirmatory Faactor Analysis (CFA) was also performed using AMOS 24.0 software to assess construct validity, composite reliability (CR), convergent validity, and discriminant validity. A number of indices, including the chi-square/df ratio ( < 3.0), the Tucker-Lewis Index (TLI > 0.90), the Comparative Fit Index (CFI > 0.90), the Root Mean Square Error of Approximation (RMSEA < 0.08), and the Standardized Root Mean Square Residual (SRMR < 0.08), were used to evaluate the model fit.

Result

Characteristics of the study population

This study enrolled 531 participants, comprising a cohort of 225 nursing students from a university in Beijing and 306 nursing undergraduates from a university in Hebei province.Specific data could be seen as detailed in Table 1.

Table 1.

Demographic characteristics of the samples of a university in Beijing (N = 225) and a university in Hebei province (N = 306)

Characteristics	Categories	A university in Beijing province (n = 225)	%	A university in Hebei province(n = 306)	%
Gender	Female	171	76.0	283	92.5
	Male	54	24.0	23	7.5
Year of enrollment	2021	23	10.2	225	73.5
	2022	69	30.7	37	12.2
	2023	88	39.1	38	12.4
	2024	45	20.0	6	2.0
Participation in innovation-related competitions	Yes - Won awards	41	18.2	65	21.2
	Yes - No awards	63	28.0	80	26.1
	No	121	53.8	161	52.6
Participation in clinical internship	Yes	206	91.6	277	90.5
	No	19	8.4	29	9.5
Exposure to innovation-related courses	Yes	167	74.2	218	71.2
	No	58	25.8	88	28.8

Validity analysis

This study implemented a double-sample validation strategy, utilizing geographically convenient sampling to assign all participants from a Beijing university (n = 225) to exploratory factor analysis (EFA) and those from a Hebei university (n = 306) to confirmatory factor analysis (CFA). This non-randomized grouping was strategically adopted both to accommodate data collection feasibility and to ensure the complete sample independence required for cross-validation.To assess group equivalence, we conducted comprehensive baseline comparisons including chi-square tests for categorical variables and independent samples t-tests for continuous variables. Results showed no significant differences between the groups (all p > 0.05). The systematic separation of analytical samples through geographically equivalent groups ensured external validation of the scale’s psychometric properties, thereby enhancing methodological robustness.

Linguistic validity

Due to existing instruments are constrained by insufficient cultural congruence within Chinese societal contexts and inadequate construct alignment with nursing students’ competencies in healthcare innovation,our research team made the following modifications based on expert consultation (the expert panel comprises of professors (n = 1), associate professors (n = 2), lecturers (n = 4), chief nursing supervisors (n = 1), doctoral candidate (n = 1), master’s student (n = 1), and clinical nurses (n = 5)),preliminary survey results,cognitive interviewing and intra-group discussion of the Chinese version of CIEP. ① It was changed from “I allow myself to think boldly about solutions when I encounter problems.” to “我允许自己在遇到问题时大胆地想解决方法。” increasing the content’s relevance to the assessment of innovation effectiveness and capabilities. ② “I can apply what I have learned to other situations and effectively find connections between things, so as to get new thinking directions”was revised to“ 我能够举一反三，有效寻找事情间的联系，从而得到新的思考方向。”making the expression more in line with our language habits. ③ “I am able to constantly reflect and adjust my assumptions”.was revised to“我能够不断反思调整自己的假设。” ④ The two items “I avoid criticizing other people’s ideas in their infancy.” and “I quietly and patiently let people fully describe their ideas.” were merged and revised to: “当听到他人尚未成熟的想法时，我会安静，耐心地让别人充分表达自己的想法，而不是进行批评。”and so on.The expert committee’s iterative translation process, combined with the validation of cognitive interviews and preliminary survey, provided a three-tiered safeguard for ensuring the semantic equivalence of the adapted CIEP. Ultimately, the final scale was refined from 37 to 31 items.The target language version of the scale was reviewed and approved by all experts to ensure the accurate conveyance of meaning.The final adjusted items are shown in Table 2.

Table 2.

Scale items of the Chinese version of CIEP

Creative Consciousness	I think there’s always a better way to solve the problem.
	I am good at solving problems by reading widely.
	I allow myself to think boldly about solutions when I encounter problems.
	I’m interested in the cutting edge of technology and new inventions.
	I would question the conventional idea.
	I like to learn examples of “divergent thinking”.
	I love researching difficult problems.
	I deem I have a strong sense of innovation and am willing to try new ways.
	I get satisfaction from coming up with new ideas and solving problems.
Pattern Breaking Skills	I can break conventional thinking by deep thinking and other methods.
	I’m good at divergent thinking.
	When faced with difficult challenges, I can find a bran-new perspective.
	I can identify traditional practices or ideas or ones lack of creativity.
	I can apply what I have learned to other situations and effectively find connections between things, so as to get new thinking directions.
	I am able to constantly reflect and adjust my assumptions.
	When I encounter a problem that is difficult to find an answer to, I will ask a few questions to promote thinking.
	I will be careful to draw conclusions or make inferences when the issue is controversial.
	I always find new trends or developments in my field of study.
Critical Thinking Ability	I believe tests that require thinking rather than relying solely on memory are more suitable for me.
	I enjoy exploring how things work.
	I like learning challenging subjects.
	I believe the essence of things is consistent with their appearance.
	When dealing with difficult problems, I think the first step is to identify the root cause.
	All the views I hold must be supported by evidence.
Idea Nurturing Ability	I encourage people to maintain enthusiasm for their ideas.
	I strive to create an atmosphere most conducive to inspiration.
	I provide others with feasible methods or approaches for investigation.
	I regularly reflect on and record my learning ideas to foster the development of innovative thinking.
	I focus on the positive aspects of others’ ideas and think about them carefully.
	I aim to make my feedback on suggestions and ideas as constructive as possible.
	Whenever possible, I try to consider different ideas and opinions together.

Content validity

Expert evaluation revealed that 25 items of the original scale demonstrated item-level content validity indices (I-CVI) exceeding the 0.80 threshold, confirming their fundamental appropriateness for the target population. Five items initially fell below the critical I-CVI value of 0.78 but attained expert consensus following methodological refinement and iterative validation. The revised scale achieved a scale-level content validity index (S-CVI) of 0.964, evidencing superior overall content validity.

Guided by expert panel recommendations, the dimensional framework underwent substantial restructuring. The optimized instrument now comprises four core dimensions: Creative Consciousness, Pattern Breaking Skills, Critical Thinking Ability, and Idea Nurturing Ability. This configuration streamlines the original structure by eliminating the dimensions of Levels of Curiosity, Willingness to Experiment and Take Risks, Courage and Resilience Levels, and Energetic Persistence, while strategically incorporating the Critical Thinking Ability dimension. This reconceptualized architecture demonstrates enhanced congruence with the cognitive-behavioral profiles and educational objectives characteristic of undergraduate nursing students in China’s higher education context.

Exploratory factor analysis (EFA)

Prior to examining reliability, we conducted exploratory factor analysis to investigate the underlying structure of the scale using the local dataset.A total of 225 undergraduate nursing students from grades 1 to 4 were randomly sampled from a university in Beijing. (See Table 1 for details)

Factor analysis suitability

The Kaiser-Meyer-Olkin measure verified sampling adequacy (KMO = 0.901),and Bartlett’s test of sphericity was significant (χ² = 4554.527, df = 465, p < 0.001). This indicates that the data is suitable for factor analysis. The factor matrix after rotation is shown in the Table 3.

Table 3.

Rotated factor matrix of the Chinese version of CIEP

Items	Factor 1 (Idea Nurturing Ability)	Factor 2 (Pattern Breaking Skills)	Factor 3 (Creative Consciousness)	Factor 4 (Critical Thinking Ability)
CX1	-	-	0.67	-
CX2	-	-	0.76	-
CX3	-	-	0.77	-
CX4	-	-	0.80	-
CX5	-	-	0.59	-
CX6	-	-	0.79	-
CX7	-	-	0.78	-
CX8	-	-	0.83	-
CX9	-	-	0.74	-
DP1	-	0.77	-	-
DP2	-	0.72	-	-
DP3	-	0.72	-	-
DP4	-	0.70	-	-
DP5	-	0.79	-	-
DP6	-	0.76	-	-
DP7	-	0.69	-	-
DP8	-	0.75	-	-
DP9	-	0.70	-	-
PP1	-	-	-	0.76
PP2	-	-	-	0.74
PP3	-	-	-	0.74
PP4	-	-	-	0.66
PP5	-	-	-	0.74
PP6	-	-	-	0.72
PY1	0.89	-	-	-
PY2	0.88	-	-	-
PY3	0.90	-	-	-
PY4	0.84	-	-	-
PY5	0.86	-	-	-
PY6	0.88	-	-	-
PY7	0.87	-	-	-

Note: Factor loadings < 0.40 are not displayed. CX = Creative Consciousness; DP = Pattern Breaking Skills; PP = Critical Thinking Ability; PY = Idea Nurturing Ability. Extraction method: Principal Component Analysis. Rotation method: Promax with Kaiser normalization. Rotation converged in 6 iterations

Factor extraction

Principal factor analysis with varimax rotation yielded 4 factors with eigenvalues > 1, explaining 62.7% (more than 60%) of total variance, It can be seen from the table that there is no cross-loading among the measurement items of each item, and the principal factor after rotation corresponds to the preset variable one by one, where factor 1 corresponds to the Idea nurturing ability, and factor 2 corresponds to the Pattern breaking skills. Factor 3 corresponds to Creative consciousness and factor 4 corresponds to Critical thinking skills. The detailed information could be seen in Table 4.

Table 4.

Total variance explained

Comp- onent	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	8.207	26.475	26.475	8.207	26.475	26.475	5.627	18.151	18.151
2	5.245	16.919	43.394	5.245	16.919	43.394	5.253	16.945	35.096
3	3.438	11.090	54.485	3.438	11.090	54.485	5.213	16.817	51.913
4	2.547	8.216	62.700	2.547	8.216	62.700	3.344	10.788	62.700

Confirmatory factor analysis (CFA)

In the first stage, a CFA model was created (as shown in Fig. 1) that included four latent factors.

Fig. 1.

Confirmatory factor analysis model of the Chinese version of CIEP. Note: The model includes four latent factors (ovals) and 31 observed items (rectangles). Standardized factor loadings are displayed; all loadings are significant at p < 0.001. CX = Creative Consciousness; DP = Pattern Breaking Skills; PP = Critical Thinking Ability; PY = Idea Nurturing Ability”

Construct validity, composite reliability (CR), convergent validity, and discriminant validity were assessed in the second stage using Amos24.0.A random sample of 306 first- through fourth-grade undergraduate nursing students from a university in the province of Hebei was taken.(See Table 1 for details).

Construct validity

By fitting the structural model of the scale, the results indicated that the model had a good fit: the relative chi-square CMIN/DF = 1.884, Goodness of Fit Index (GFI) = 0.866, Incremental Fit Index (IFI) = 0.925, Comparative Fit Index (CFI) = 0.925, and Root Mean Square Error of Approximation (RMSEA) = 0.051.(Table 5) Confirmatory factor analysis (CFA) revealed that the standardized factor loadings of the scale items ranged from 0.521 to 0.844 (Table 6).

Table 5.

Model goodness of fit indices of the CIEP

Indices	CMIN/DF	GFI	IFI	CFI	RMSEA	SRMR
Value	1.884	0.866	0.925	0.925	0.051	0.033

CMIN, Chi-square; DF, Degrees of Freedom; GFI, Goodness of Fit Index; IFI, Incremental Fit Index; CFI, Comparative Fit Index; RMSEA, Root Mean Square Error of Approximation; SRMR, Standardized Root Mean Square Residual

Table 6.

Standardized regression weights

Items		Factors	Estimate
CX1	<—	Creative consciousness	0.722
CX2	<—	Creative consciousness	0.713
CX3	<—	Creative consciousness	0.734
CX4	<—	Creative consciousness	0.745
CX5	<—	Creative consciousness	0.662
CX6	<—	Creative consciousness	0.680
CX7	<—	Creative consciousness	0.736
CX8	<—	Creative consciousness	0.727
CX9	<—	Creative consciousness	0.676
DP1	<—	Pattern breaking skills	0.762
DP2	<—	Pattern breaking skills	0.577
DP3	<—	Pattern breaking skills	0.686
DP4	<—	Pattern breaking skills	0.610
DP5	<—	Pattern breaking skills	0.709
DP6	<—	Pattern breaking skills	0.577
DP7	<—	Pattern breaking skills	0.545
DP8	<—	Pattern breaking skills	0.571
DP9	<—	Pattern breaking skills	0.664
PP1	<—	Critical thinking skills	0.694
PP2	<—	Critical thinking skills	0.829
PP3	<—	Critical thinking skills	0.664
PP4	<—	Critical thinking skills	0.521
PP5	<—	Critical thinking skills	0.844
PP6	<—	Critical thinking skills	0.712
PY1	<—	Idea nurturing ability	0.796
PY2	<—	Idea nurturing ability	0.757
PY3	<—	Idea nurturing ability	0.769
PY4	<—	Idea nurturing ability	0.779
PY5	<—	Idea nurturing ability	0.726
PY6	<—	Idea nurturing ability	0.772
PY7	<—	Idea nurturing ability	0.792

Composite reliability (CR) and convergent validity

The scale’s internal consistency and convergent validity were evaluated using composite reliability(CR)and average variance extracted(AVE).CR ratings exceeded the 0.70 criterion and ranged from 0.862 to 0.911, indicating good internal consistency. AVE levels above 0.36 are considered acceptable limits, and values above 0.5 are the ideal standard for the best validity evaluation [13]. The research cohort’s AVE values, which ranged from 0.406 to 0.593, were all above 0.36 and primarily over the suggested 0.50, suggesting that the latent variables account for a significant amount of the variation in their individual items. (Table 7).

Table 7.

AVE and CR

Factors	AVE	CR
Creative consciousness	0.506	0.903
Pattern breaking skills	0.406	0.874
Critical thinking skills	0.517	0.862
Idea nurturing ability	0.593	0.911

Discriminant validity

Discriminant validity was evaluated by comparing the square root of AVE for each factor with its correlations to other factors (Table 8). The square roots of AVE (diagonal values) were greater than the inter-factor correlations (off-diagonal values), confirming that each factor has good discriminative validity.

Table 8.

Discriminant validity

	Creative consciousness	Pattern breaking skills	Critical thinking skills	Idea nurturing ability
Creative consciousness	0.711
Pattern breaking skills	0.289	0.637
Critical thinking skills	0.027	0.607	0.719
Idea nurturing ability	0.000	0.510	0.254	0.770

These results confirm that the scale measures distinct yet related dimensions of innovation ability.

Reliability analysis

Reliability analysis is a key step in verifying the stability and consistency of the scale. In this study, we primarily examined the scale’s reliability from the perspective of internal consistency.

Internal consistency

The internal consistency of the scale was assessed by calculating Cronbach’s α coefficient. The results showed that the overall Cronbach’s α coefficient of the scale was 0.894, indicating a high level of internal consistency, which ensures that the scale reliably measures the core characteristics of the research subjects. Generally, the higher the Cronbach’s α value, the stronger the correlation between the items in the scale. This scale’s internal consistency has reached an excellent standard. To further examine the independence of the scale’s factors, we also calculated the sub-factor Cronbach’s α values, and the results showed that most factors had values greater than 0.60, indicating that each factor has good internal consistency and can independently measure its specific construct (Table 9).

Table 9.

Internal reliability

Factors	Items	Item-total correlation	Alpha coefficient if item deleted	Alpha coefficient
Creative consciousness	CX1	0.587	0.897	0.902
	m	CX2	0.687
		CX3	0.686
		CX4	0.729
		CX5	0.505
		CX6	0.727
		CX7	0.731
		CX8	0.775
		CX9	0.675
Pattern breaking skills	DP1	0.745	0.892	0.907
	DP2	0.679	0.896
	DP3	0.682	0.896
	DP4	0.633	0.900
	DP5	0.733	0.892
	DP6	0.689	0.896
	DP7	0.648	0.899
	DP8	0.678	0.896
	DP9	0.657	0.898
Critical thinking skills	PP1	0.630	0.801	0.833
	PP2	0.630	0.801
	PP3	0.609	0.805
	PP4	0.523	0.822
	PP5	0.618	0.803
	PP6	0.621	0.803
Idea nurturing ability	PY1	0.872	0.953	0.960
	PY2	0.870	0.953
	PY3	0.892	0.951
	PY4	0.817	0.957
	PY5	0.837	0.955
	PY6	0.869	0.953
	PY7	0.862	0.953

Retest reliability

Test-retest reliability analysis was used to thoroughly analyze the instrument’s temporal stability. Data from 30 participants who completed both administrations (students who took part in the preliminary survey) were analyzed using two-way mixed-effects intraclass correlation coefficients (ICC [1, 3]) with absolute agreement, utilizing a 14-day interval (T1–T2) as advised by psychometric standards [14]. With an ICC of 0.846, 95% CI [0.701, 0.923], p < 0.001, the results showed good consistency, surpassing the minimum requirement of 0.70 for longitudinal educational research [15].

Discussion

Adapted four-dimensional structure demonstrates strong psychometric properties

Through thorough psychometric assessments and confirmatory factor analysis,our team adapted and validated the Chinese adaptation of CIEP, demonstrating its great construct validity and reliability.The improved four-dimensional framework, which includes 1) Creative Consciousness (active engagement through curiosity); 2) Pattern Breaking Skills(going beyond traditional problem-solving); 3) Critical Thinking Ability(systematic information processing for decision optimization)and 4) Idea Nurturing Ability (collaborative ideation through constructive feedback),shows significant theoretical relevance to innovation pedagogy.This adapted instrument demonstrates superior contextual appropriateness for Chinese undergraduate nursing students compared to the original seven-dimensional version. It achieves enhanced ecological validity through meticulous alignment with local educational objectives, clinical innovation contexts, and cultural epistemology. The reconceptualized items exhibit improved semantic congruence with Eastern logical paradigms and collectivist learning orientations, while maintaining cross-cultural conceptual equivalence. [16] This ensures precise measurement of innovation competencies within China’s unique nursing education ecosystem, effectively balancing theoretical sophistication with pragmatic applicability across academic and clinical domains.

The CIEP cultural adaptation is consistent with the requirements of Chinese nursing education

Our adaptation process identified that several original dimensions (Levels of Curiosity, Willingness to Experiment and Take Risks, Courage and Resilience Levels, and Energetic Persistence) were designed for broader populations,which limited their applicability to nursing, according to our adaption process [5, 17]. To address this, we optimized the scale via:

Critical thinking ability is introduced as core nursing innovation competency

Critical thinking is essential to nursing practice, especially for clinical decision-making and evidence-based treatment, according to numerous research [18, 19]. Its importance for evaluating nursing innovation in our setting was validated by expert panel evaluations (mean content validity index = 0.964), where it facilitates: 1) diagnostic reasoning through hypothesis evaluation, 2) Optimize learning methods through reflection, and 3) systematic problem-solving in clinical scenarios.This is in line with the findings of Nuampa [20] and Moropa, and Moropa’s study [21] highlights the importance of critical thinking for the capacity to address real-world issues in clinical practice.,.Kim’s study [22] also points out CTD (critical thinking disposition) is important as research barriers to engage effectively in EBP (evidence-based practice). 30 students participated in cognitive interviews, which showed that these items had good understanding accuracy.

Strategic elimination of some dimensions enhances innovation capacity relevance

Levels of Curiosity and the fundamental concept of Creative Consciousness were shown to overlap on several dimensions when Delphi expert reviews and cognitive debriefing interviews were taken into account [23, 24]. Furthermore, Courage and Resilience Levels and Energetic Persistence showed poor empirical congruence with measures of innovation efficiency.Similar to this, researchers used the LDA topic model to identify 20 prospective dimensions for the generativity study of digital innovation, but they only kept four essential aspects that were closely tied to innovation, like “technological programmability” and “social interaction” [25]. Therefore, all the dimensions listed above is eliminated or combined.

Revised measurement characteristics substantiating scale validity

The robustness of the measurement model was confirmed by various verification methods. Exploratory and confirmatory factor analyses collectively demonstrated a stable four-factor structure explaining 62.7% of total variance, with all items loading significantly (0.521–0.844, p < 0.001) on their theoretical dimensions without substantive cross-loadings. The revised CIEP model exhibited exceptional fit indices (χ²/df = 1.884, GFI = 0.866, CFI = 0.925, RMSEA = 0.051), surpassing rigorous psychometric thresholds for model excellence. Composite reliability coefficients (0.862–0.911) indicated strong internal consistency across dimensions, while average variance extracted values (0.406–0.593) established convergent validity. Although the Pattern Breaking Skills dimension’s AVE (0.406) marginally fell below 0.50, its acceptable CR (0.874) and discriminant validity evidence (the square roots of AVE > inter-dimension correlations) maintained psychometric justification. The measurement model ultimately satisfied all critical criteria - structural validity, reliability, convergent validity, and discriminant validity - confirming its statistical adequacy and theoretical coherence for operationalizing the target construct.

Study limitations and future research directions

The methodological constraints of convenience sampling and their implications for the generalizability of findings are acknowledged. Given the use of convenience sampling, sample representativeness is limited, and selection bias may exist; subsequent investigations will therefore expand both the geographic scope and sample size. Such bias could manifest as over-representation of specific geographic, educational, or socioeconomic groups. To mitigate the inherent limitations of non-probability sampling, a stratified recruitment strategy was employed, encompassing all academic levels and deliberately diversifying participant selection across nursing student typologies. Additionally, detailed sample characterization (e.g., gender, experience with innovative activities,and year of enrollment) enhances the study’s content validity and credibility.

While the cross-sectional design inherently restricts observation of longitudinal trajectories in innovation competency, this limitation was partially addressed through a multi-phase preparatory process—including preliminary investigations, expert consultations, and cognitive interviews—thereby introducing temporal dimensions to data collection. Geographic representativeness is partially offset by the diverse regional backgrounds of participants: although sampling was limited to Beijing and Hebei provinces, nursing undergraduates originate from across China, strengthening the sample’s geographic diversity.

Regarding measurement validity, while criterion-related validation against established innovation assessment tools was not conducted, confirmatory factor analysis via structural equation modeling substantiated the scale’s psychometric robustness, demonstrating satisfactory construct validity across all theoretical dimensions in the target population. Future research should prioritize longitudinal tracking of innovation capability maturation and establish empirical correlations between scale-derived metrics and objective clinical innovation indicators (e.g., nursing patent authorization rates, evidence-based practice implementation ratios, and quality improvement initiative adoption frequencies). These methodological enhancements would strengthen the ecological validity framework and enable more robust clinical interpretations of innovation assessment outcomes.

Conclusion

After the adaptation, CIEP has four dimensions and 31 items, which have been explored and verified with good results.The application of this scale in nursing education demonstrates substantial academic promise and transformative potential. Its theoretical framework not only provides an innovative lens for contemporary educational quality assessment but may also catalyze paradigm shifts in professional nursing talent cultivation models. Through continuous refinement of the measurement system, future developments could establish comprehensive dynamic monitoring networks encompassing the entire innovation competency development cycle, thereby informing evidence-based policy formulation for educational optimization. As interdisciplinary institutional collaborations deepen, the instrument’s utility may extend into clinical practice and continuing education domains, fostering synergistic integration within the education-practice continuum.

Within global academic discourse, culturally adapted iterations of the scale hold potential to serve as pivotal tools for international comparative studies in innovative nursing education, offering transferable assessment frameworks adaptable to diverse healthcare systems worldwide. These advancements collectively propel the evolution of nursing education evaluation systems, transitioning from conventional competency evaluation toward empowerment-oriented systems that actively promote professional sustainability. Such systemic progression ultimately contributes to the realization of precision education paradigms within the nursing discipline, bridging theoretical constructs with practical implementation in healthcare innovation ecosystems.

Author contributions

Siqi.Ye. conducted writing and polishing in English. Xinyu.Zhao. was responsible for writing and revision.Zhehao.Hu. and Xinyue.Zhang. shouldered literature retrieval and questionnaire collection. Meiqi.Meng. gave guidance and writing. Xuejing.Li. and Yufang.Hao. proposed ideas and provided guidance and modification. All authors reviewed the manuscript.

Funding

The Traditional Chinese Medicine Innovation Team and Talent Support Program—National Traditional Chinese Medicine Multidisciplinary Cross-Innovation Team Project—provided financial assistance for this study. The study’s design, data collection, analysis, and interpretation, as well as manuscript preparation, were all independent of the funding component.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

This study was approved by the Medical Ethics Committee of Beijing University of Chinese Medicine (approval number: 2025BZYLL0103). All procedures were conducted in accordance with the Declaration of Helsinki and relevant national ethical guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.