Current status of career adaptability among Chinese cardiovascular specialist nurses: a latent profile analysis

Abstract

Aim

This study aimed to explore the career adaptability status of cardiovascular specialist nurses (CSNs) through latent profile analysis (LPA), identify distinct subgroups and their demographic features, and determine factors influencing different adaptability categories.

Background

CSNs play a vital role in treating and rehabilitating patients with cardiovascular conditions. However, the existing literature offers limited insights into the career adaptability of CSNs in China.

Methods

A multicenter, cross-sectional survey involving 659 Chinese CSNs was conducted. LPA was utilized to classify career adaptability profiles based on responses to the Career Adaptation Abilities Scale Short Form (CAAS-SF). Influencing factors were assessed using the Conditions of Work Effectiveness Questionnaire-II (CWEQ-II) and the General Self-Efficacy Scale (GSES). Differences among identified profiles were analyzed through ANOVA, chi-square tests, and multinomial logistic regression to explore relevant socio-demographic characteristics and influencing variables.

Results

A four-profile model provided the best fit, identifying groups labeled as “high adaptability” (Class 4, n = 68, 10.5%), “good adaptability” (Class 3, n = 285, 42.6%), “moderate adaptability” (Class 1, n = 269, 41.2%), and “low adaptability” (Class 2, n = 37, 5.7%). Regression analysis indicated that highest educational level, monthly frequency of night shifts, opportunity, resources, informal empowerment, and self-efficacy were influencing factors associated with profile membership. CSNs working 1~2 night shifts per month (OR = 2.226, 95% CI: 1.014, 4.887) or possessing a bachelor’s degree (OR = 2.090, 95% CI: 1.440, 3.035) exhibited a greater likelihood of belonging to Class 3. Those reporting more opportunities (OR = 1.113, 95% CI: 1.001, 1.238; OR = 1.236, 95% CI: 1.028,1.485) tended toward membership in Classes 3 and 4. Furthermore, CSNs with greater resources (OR = 0.777, 95% CI: 0.620, 0.975; OR = 1.360, 95% CI: 1.116, 1.656) were significantly associated with membership in Classes 1 and 4. Additionally, higher informal empowerment (OR = 1.095, 95% CI: 1.001, 1.198) was associated with Class 3 membership. Enhanced self-efficacy (OR = 1.112, 95% CI: 1.069, 1.159; OR = 1.358, 95% CI: 1.268,1.455) increased the likelihood of classification into Classes 3 and 4.

Conclusions

These findings provide evidence to assist nursing administrators in developing training programs to enhance CSNs’ career adaptability. The variables identified as associated with profile membership may enable more tailored training strategies.

Background

Cardiovascular diseases (CVDs) remain the predominant cause of global mortality, significantly contributing to the worldwide health burden. Approximately 19.8 million deaths globally were attributed to CVDs in 2022 alone, resulting in roughly 396 million years of life lost and an additional 44.9 million years lived with disability (YLD) [1]. Furthermore, forecasts indicate that from 2025 to 2050, the prevalence of CVDs will rise by approximately 90.0%, crude mortality rates will increase by 73.4%, and crude disability-adjusted life years (DALYs) will grow by 54.7%, ultimately culminating in an estimated 35.6 million deaths from cardiovascular-related causes by 2050 [2]. Consequently, this increasing disease burden places substantial pressure on healthcare infrastructures, emphasizing the necessity of a highly skilled, adaptable nursing workforce capable of addressing the escalating demands for specialized care [3]. Against this backdrop, cardiovascular specialized nurses (CSNs) are instrumental in mitigating this burden by delivering quality patient care, facilitating patient education, and supporting rehabilitation processes [4].

Distinct from general nurses, CSNs are specialized professionals with clinical nursing experience in CVDs. They have undergone systematic theoretical education and practical training, obtained relevant qualifications, and can competently use professional knowledge and skills to provide specialized services and guidance [5]. Research underscores that CSNs play a prominent role in the care of cardiovascular diseases. Through the early identification of cardiovascular risk factors and implementation of evidence-based intervention strategies, they significantly improve health outcomes and reduce readmission rates [6, 7]. Nevertheless, the role of CSNs faces several challenges, including professional capability aspects (standardization and training, and adapting to emerging skills), systemic and environmental factors (disease burden, resource constraints, collaboration, and coordination), workforce health (mental health, occupational burnout), and leadership empowerment [8, 9]. In addition, a study conducted in China indicates that career success among CSNs requires further improvement [10].

Against the backdrop of these challenges, career adaptability, defined as an individual’s capacity to navigate occupational transitions successfully, manage work-related challenges effectively, and achieve career success [11], emerges as critical for CSNs. Notably, it has been established that healthcare professionals with higher career adaptability are better equipped to mitigate occupational pressures, minimize burnout, and enhance their overall job satisfaction [12]. Within the nursing field, characterized by complex and ever-changing demands, high career adaptability is particularly crucial, enabling nurses to manage dynamic work environments, address challenges proactively, and foster positive career development [13]. According to research by Ulupinar [14], 53.5% of newly qualified general nurses encounter difficulty adapting to nursing work, while 42.5% express intentions to exit the profession. Similarly, another study revealed nurses currently possess only moderate career adaptability [15]. Moreover, previous literature suggests that nurses’ career adaptability is significantly impacted by individual attributes, available supportive resources, and workplace conditions [16, 17]. However, despite existing studies addressing nurses’ career adaptability in general, research specifically examining the current status and influencing factors among CSNs remains limited.

The empowerment and self-efficacy further contribute to understanding the career adaptability of CSNs. Empowerment, as described in the existing literature, encompasses structural and psychological dimensions [18]. Kanter defined structural empowerment as the capacity to utilize resources effectively and accomplish objectives through accessing necessary information, receiving adequate support, acquiring resources, and having opportunities for professional growth and development [19]. Nurses’ access to empowering structures positively affects various quality outcomes, including speak-up behavior, quality, effectiveness, safety, efficiency, organizational commitment, burnout, job satisfaction, work engagement, and patient-centered care [20–22].

Self-efficacy refers to an individual’s perceived ability to effectively regulate their motivation, behaviors, and social environments. Bandura’s pioneering research defined self-efficacy as a person’s confidence in their ability to execute actions essential for attaining desired performance outcomes [23, 24], highlighting its critical contribution to enhancing career adaptability [25]. As originally conceptualized by Bandura, self-efficacy represents the perception individuals hold regarding their capability to undertake the actions necessary to achieve specific objectives, thus serving as a precursor influencing intention formation [26]. Individuals with enhanced self-efficacy tend to more readily formulate goal-directed intentions compared to those who exhibit lower confidence levels. Importantly, higher levels of self-efficacy demonstrate a positive association with improved career adaptability [27].

This research was informed by Career Development Theory (CDT) [28], which posits that career adaptability arises from the interaction between individual characteristics and environmental influences. In this study, individual characteristics were operationalized as self-efficacy (i.e., CSNs’ confidence in managing work-related tasks and challenges), and environmental influences were represented by structural empowerment (i.e., access to workplace support, resources, and opportunities). The aim was to examine the influence of these two factors on CSNs’ career adaptability.

To fulfill the research objectives, latent profile analysis (LPA) was adopted as the primary statistical technique in this study. As a person-centered statistical method, LPA can classify individuals into distinct subgroups according to shared behavioral patterns or characteristic traits [28–30]. This analytical method was specifically chosen for its effectiveness in revealing variations in career adaptability among CSNs by distinguishing meaningful subgroups based on common attributes. Compared to traditional analytic techniques, such as factor analysis or clustering, LPA is characterized by a model-based framework that not only manages measurement errors but also probabilistically assigns participants into latent classes. This methodological approach has previously been applied in nursing research examining distinct subgroups related to psychosomatic symptoms, presenteeism, and quality of life. By leveraging the strengths of LPA, this study can identify subtle patterns in career adaptability potentially undetectable by conventional methods, thus informing the design of targeted interventions aimed at optimizing outcomes.

Materials and methods

Study design

A cross-sectional study employing purposive sampling methods was performed between May and June 2024, covering ten hospitals situated in different regions of China. All stages of the research strictly complied with ethical principles stipulated by the Karolinska Institute, the Declaration of Helsinki (1964), and its subsequent amendments. Ethical approval for the current study was obtained from the Ethics Committee at the Second Affiliated Hospital of Air Force Medical University (Approval No. k202403-02). Prior to participating in the online questionnaire, informed consent was obtained from all participants.

Participants

The study targeted Chinese CSNs from May to June 2024. Participants met the following inclusion criteria: (a) holding a cardiovascular specialist nurse certificate at least one year and being engaged in clinical nursing related to cardiovascular care; and (b) providing informed consent and voluntarily participating. Nurses absent from work or on sick leave during the survey period were excluded.

Data collection

The minimum required sample size was calculated based on the formula: N = Z² ∗ S²/d². Here, Z represents the quantile of the standard normal distribution corresponding to the confidence level. For a 95% confidence interval, the value of Z is 1.96. This is because the two-tailed areas corresponding to a 95% confidence interval are each 2.5%, and by referring to the standard normal distribution table, the quantile is 1.96. S is the standard deviation, which was determined to be 0.8 based on experience from similar studies [31]. d is the allowable error rate, set at 10% (0.1), which reflects the maximum allowable error range between the sample statistic and the population parameter that we expect. According to these parameters, a minimum of 246 participants were necessary. Considering a 20% nonresponse rate, we distributed 296 questionnaires. We conducted an anonymous online survey using Wen Juanxing, an online data collection platform. Administrators responsible for participants received training via telephone and WeChat before survey distribution. Participants completed the online survey by scanning a QR code via WeChat, independently and without researcher or administrator oversight. Ultimately, 659 questionnaires were collected.

Measures

Data were collected through online questionnaires, including a General Information Questionnaire, the CAAS-SF, the CWEQ-II, and the GSES.

General information questionnaire

This self-designed questionnaire collected demographic and sociological information.

CAAS-SF

Career adaptability was assessed using the Career Adaptation Abilities Scale Short Form (CAAS-SF) [32, 33]. This instrument includes four sub-dimensions: (A) career focus, (B) career control, (C) career curiosity, and (D) career confidence, each composed of three items. Participants responded to the items using a Likert-type scale, scoring from 1 (strongly disagree) to 5 (strongly agree). Higher scores for both total and individual dimensions reflect stronger career adaptability. This scale has extensive applications within nursing research and exhibits high reliability and validity. In this study, Cronbach’s α coefficients ranged from 0.83 to 0.91 for the subscales, 0.95 for the total scale.

CWEQ-II

Structural empowerment was measured using the Conditions for Work Effectiveness Questionnaire-II (CWEQ-II) [34, 35]. This scale comprises six distinct dimensions: (A) opportunity (3 items), (B) information (3 items), (C) support (3 items), (D) resources (3 items), (E) formal empowerment (3 items), and (F) informal empowerment (4 items). Respondents rated each item using a five-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater perceptions of managerial empowerment. Cronbach’s α coefficients in the original CWEQ-II instrument ranged from 0.69 to 0.88 across subscales and reached 0.89 for the total scale. In the present study, Cronbach’s α coefficients varied from 0.78 to 0.88 for individual sub-dimensions, and the overall scale achieved an alpha coefficient of 0.94.

GSES

General self-efficacy was assessed using the General Self-Efficacy Scale (GSES) [36, 37], which comprises 10 items scored on a four-point Likert scale ranging from 1 (completely incorrect) to 4 (completely correct). Higher cumulative scores reflect greater perceived self-efficacy. The original version of the scale demonstrated a Cronbach’s α coefficients of 0.87, while the current study recorded a Cronbach’s α value of 0.94.

Statistical analysis

SPSS (version 24.0) and Mplus (version 8.4) were used for data analysis. Descriptive statistics were conducted for all variables. Continuous variables are presented as mean ± standard deviation (SD), and categorical variables are presented as frequencies and percentage. To accomplish the study aims, we identifed latent profile subgroups of CSNs’ career adaptability based on the four dimensions of the CAAS-SF. Based on a specific set of factors, LPA enables the identification of latent subgroups within the population. Initiall, we determined the number of subgroups using the Bayesian information criterion (BIC), entropy, and the bootstrapped likelihood ratio test (BLRT). Lower values of BIC indicate better model fit, as this measure accounts for model fit and parsimony. Individual classification accuracy is referred to as entropy (values close to 1 are preferred). Significant p-values indicate that the k-class model has significantly better fit compared to the (k-1) class model based on the BLRT. Meanwhile, Chi-square analyses were employed to examine the latent class distribution and its associations with sociodemographic characteristics. Differences in structural empowerment and self-efficacy scores across the identified career adaptability profiles were assessed using one-way analysis of variance (ANOVA), with post-hoc comparisons conducted by employing the Least Significant Difference (LSD) test. Furthermore, we performed multinomial logistic regression to investigate the variables infuencing profile membership. A statistically significant difference was accepted at a two-sided p-value of < 0.05.

Results

Fit statistics for latent profiles

LPA was performed under assumptions of equal variance and zero covariance across latent subgroups, testing models containing between 1 and 5 potential profiles. Table 1 summarizes these results. Although the five-class solution showed the best entropy and lowest ABIC and AIC values, it raised methodological concerns because one profile accounted for only 2.1% of the entire sample [38]. Such a small subgroup could produce unstable parameter estimates and heightened sensitivity to minor data fluctuations, making meaningful interpretation difficult and potentially undermining external validity. In contrast, the four-class solution exhibited strong model fit indices, entropy comparable to the five-class solution, and considerably lower ABIC and AIC values than those with fewer classes. Importantly, each subgroup within the four-profile solution contained a sufficiently large proportion of participants, ensuring stable estimations and interpretable outcomes. Thus, based on statistical criteria, model parsimony, interpretability, and substantive and statistical stability, the four-class solution was ultimately adopted for further analysis.

Table 1.

Fit statistics for 1–5 latent profile models (n = 659)

Model	AIC	BIC	ABIC	Entropy	LMR-p	BLRT-p	Proportion
1-Profile	11937.835	11973.761	11948.361
2- Profile	11086.529	11144.909	11103.633	0.812	0.0004	0.0003	0.45979/0.54021
3- Profile	10453.345	10534.178	10477.027	0.888	0.0009	0.0007	0.05159/0.51442/0.43399
4- Profile	10026.803	10130.09	10057.064	0.924	0.0353	0.0327	0.40819/0.05615/0.43247/0.10319
5- Profile	9826.099	9951.84	9862.939	0.952	0.0078	0.0071	0.04401/0.02124/0.42185/0.41123/0.10167

Distribution of career adaptability in the 4-profile model

Table 2 summarizes the career adaptability scores across the four latent profiles identified in this study, with Fig. 1 providing a visual depiction. Statistical analysis revealed substantial differences among the four profiles regarding their respective mean scores on each dimension of the CAAS-SF (all p < 0.05). Among these groups, Class 1 was the second most prevalent (n = 269, 41.2%) and demonstrated a moderate level of career adaptability. Class 2, comprising the smallest proportion of respondents (n = 37, 5.7%), was distinguished by the lowest adaptability scores. Class 3, the largest subgroup (n = 285, 42.6%), demonstrated good levels of adaptability, whereas Class 4 (n = 68, 10.5%) showed the highest adaptability scores. Consequently, the four classes were labeled as follows: Class 1 “moderate adaptability”, Class 2 “low adaptability “, Class 3 “good adaptability “, and Class 4 “high adaptability” .

Table 2.

Distribution of career adaptability in the 4-profile model (N = 659), mean ± sd

	Dimensions	Total sample(n = 659)	Class1(n = 269)	Class2(n = 37)	Class3(n = 285)	Class4(n = 68)	F	p
A1	career concern	9.44 ± 2.30	8.56 ± 1.42	5.14 ± 1.80	10.06 ± 1.70	12.68 ± 2.05	213.31	< 0.001
A2	career control	10.67 ± 2.35	9.34 ± 1.18	5.41 ± 1.85	11.75 ± 1.09	14.25 ± 1.11	642.13	< 0.001
A3	career curiosity	10.49 ± 2.29	9.13 ± 0.71	4.95 ± 1.51	11.57 ± 0.86	14.32 ± 0.89	1350.39	< 0.001
A4	career confidence	11.35 ± 2.36	10.03 ± 1.31	6.43 ± 2.89	12.46 ± 1.24	14.56 ± 0.94	418.19	< 0.001
Total		41.94 ± 8.27	37.06 ± 2.73	21.92 ± 6.05	45.84 ± 2.90	55.81 ± 3.14	1327.88	< 0.001

Fig. 1.

Distribution of career adaptability in the 4-profle model

Interprofile characteristic difference

Differences among profiles regarding socio-demographic, structural empowerment and self-efficacy are shown in Tables 3 and 4. Significant differences were observed across profiles for the highest degree, professional title, position, working experience, monthly night shift frequency, monthly income, physical condition, hospital grade, opportunity, information, support, resources, formal empowerment, informal empowerment, and self-efficacy (all p < 0.05). Moreover, the LSD test revealed that the mean score of the “high adaptability” group was significantly higher than those of the other groups.

Table 3.

Interprofile characteristic differences (N = 659), N (%)

Variables	Total sample (n = 659)	Class1	Class2	Class3	Class4	χ2	P
Gender
Male	27(4.10)	7(2.60)	3(8.11)	16(5.61)	1(1.47)	5.91	0.116
Female	632(95.90)	262(97.40)	34(91.89)	269(94.39)	67(98.53)
Age
20~30 y	236(35.81)	96(35.69)	19(51.35)	102(35.79)	19(27.94)	7.67	0.264
31~40 y	370(56.15)	152(56.51)	16(43.24)	162(56.84)	40(58.82)
41~60 y	53(8.04)	21(7.81)	2(5.41)	21(7.37)	9(13.24)
Marital status
Married	565(85.74)	226(84.01)	33(89.19)	243(85.26)	63(92.65)	12.43	0.053
Unmarried	91(13.81)	43(15.99)	3(8.11)	41(14.39)	4(5.88)
Other	3(0.46)	0(0.00)	1(2.70)	1(0.35)	1(1.47)
Highest degree
Secondary vocational	9(1.37)	6(2.23)	2(5.41)	1(0.35)	0(0.00)	34.87	< 0.001
Junior college	218(33.08)	114(42.38)	14(37.84)	69(24.21)	21(30.88)
Undergraduate	429(65.10)	149(55.39)	21(56.76)	213(74.74)	46(67.65)
Graduate and above	3(0.46)	0(0.00)	0(0.00)	2(0.70)	1(1.47)
Professional Title
Nurse	119(18.06)	65(24.16)	8(21.62)	36(12.63)	10(14.71)	23.43	0.005
Nurse practitioner	292(44.31)	127(47.21)	14(37.84)	122(42.81)	29(42.65)
Nurse-in-charge	226(34.29)	72(26.77)	14(37.84)	113(39.65)	27(39.71)
Associate professor of nursing and above	22(3.34)	5(1.86)	1(2.70)	14(4.91)	2(2.94)
Position
Nurse	547(83.00)	235(87.36)	34(91.89)	225(78.95)	53(77.94)	17.13	0.047
Head nurse	106(16.08)	33(12.27)	3(8.11)	57(20.00)	13(19.12)
Deputy director of nursing department	2(0.30)	1(0.37)	0(0.00)	1(0.35)	0(0.00)
Director of nursing department	4(0.61)	0(0.00)	0(0.00)	2(0.70)	2(2.94)
Working experience
1~5 y	424(64.34)	156(57.99)	21(56.76)	203(71.23)	44(64.71)	21.27	0.012
6~10 y	163(24.73)	80(29.74)	11(29.73)	55(19.30)	17(25.00)
11~20 y	66(10.02)	30(11.15)	3(8.11)	26(9.12)	7(10.29)
≥ 20 y	6(0.91)	3(1.12)	2(5.41)	1(0.35)	0(0.00)
Employment type
Permanent	517(78.45)	220(81.78)	29(78.38)	214(75.09)	54(79.41)	3.71	0.294
Contract	142(21.55)	49(18.22)	8(21.62)	71(24.91)	14(20.59)
Monthly night shift frequency
0	166(25.19)	61(22.68)	10(27.03)	75(26.32)	20(29.41)	22.81	0.029
1 ~ 2	61(9.26)	13(4.83)	5(13.51)	36(12.63)	7(10.29)
3 ~ 4	149(22.61)	79(29.37)	5(13.51)	53(18.60)	12(17.65)
5 ~ 6	150(22.76)	57(21.19)	8(21.62)	68(23.86)	17(25.00)
≥ 7	133(20.18)	59(21.93)	9(24.32)	53(18.60)	12(17.65)
Monthly income (CNY)
< 5000	398(60.39)	185(68.77)	28(75.68)	150(52.63)	35(51.47)	28.51	0.019
5000~6999	161(24.43)	52(19.33)	5(13.51)	83(29.12)	21(30.88)
7000~9999	82(12.44)	29(10.78)	4(10.81)	39(13.68)	10(14.71)
10000~14999	14(2.12)	2(0.74)	0(0.00)	11(3.86)	1(1.47)
15000~19999	1(0.15)	0(0.00)	0(0.00)	1(0.35)	0(0.00)
≥ 20,000	3(0.46)	1(0.37)	0(0.00)	1(0.35)	1(1.47)
Physical condition
Poor	63(9.56)	21(7.81)	9(24.32)	28(9.82)	5(7.35)	33.54	< 0.001
Generally	360(54.63)	174(64.68)	21(56.76)	134(47.02)	31(45.59)
Good	236(35.81)	74(27.51)	7(18.92)	123(43.16)	32(47.06)
Hospital grade
Class­III	219(33.23)	71(26.39)	8(21.62)	113(39.65)	27(39.71)	16.01	0.014
Class­II	439(66.62)	198(73.61)	29(78.38)	171(60.00)	41(60.29)
Class­I	1(0.15)	0(0.00)	0(0.00)	1(0.35)	0(0.00)

Table 4.

Differences in structural empowerment, self-efficacy, and potential profile scores, mean ± sd

Item	Total sample (n = 659)	class1 (n = 269)	Class2 (n = 37)	class3 (n = 285)	class4 (n = 68)	F	p	LSD
Opportunity	9.09 ± 2.36	8.51 ± 1.84	6.81 ± 2.44	9.53 ± 2.19	10.82 ± 3.08	38.08	< 0.001	1 > 2, 3 > 1 4 > 1, 3 > 2 4 > 2, 4 > 3
Information	7.60 ± 2.68	7.01 ± 2.28	5.68 ± 2.29	7.95 ± 2.52	9.51 ± 3.52	26.60	< 0.001	1 > 2, 3 > 1 4 > 1, 3 > 2 4 > 2, 4 > 3
Support	8.50 ± 2.55	7.86 ± 2.01	5.95 ± 2.24	8.96 ± 2.37	10.46 ± 3.33	40.74	< 0.001	1 > 2, 3 > 1 4 > 1, 3 > 2 4 > 2, 4 > 3
Resources	8.93 ± 2.39	8.35 ± 1.93	6.30 ± 2.23	9.27 ± 2.08	11.24 ± 3.00	53.68	< 0.001	1 > 2, 3 > 1 4 > 1, 3 > 2 4 > 2, 4 > 3
Formal empowerment	8.61 ± 2.53	7.91 ± 1.96	5.95 ± 2.32	9.06 ± 2.25	10.88 ± 3.36	51.30	< 0.001	1 > 2, 3 > 1 4 > 1, 3 > 2 4 > 2, 4 > 3
Informal empowerment	11.68 ± 3.10	10.74 ± 2.38	8.62 ± 2.93	12.37 ± 2.75	14.22 ± 4.25	48.78	< 0.001	1 > 2, 3 > 1 4 > 1, 3 > 2 4 > 2, 4 > 3
Self efficacy	24.25 ± 6.30	21.69 ± 4.89	18.46 ± 5.23	25.43 ± 5.46	32.56 ± 5.73	97.83	< 0.001	1 > 2, 3 > 1 4 > 1, 3 > 2 4 > 2, 4 > 3

Variables associated with latent profle membership

A multinomial logistic regression was employed to analyze socio-demographic and contextual factors influencing the identified career adaptability profiles among CSNs. The dependent variable comprised four profile categories (Classes 1 to 4), with Class 1 designated as the reference group. Predictor variables included highest education level, frequency of monthly night shifts, availability of opportunities, resources, informal empowerment, and self-efficacy. Results of the regression analysis are displayed in Table 5. All predictors significantly influenced the likelihood of membership in specific latent profiles. CSNs reporting higher resource availability were significantly more likely to belong to Class 1 rather than Class 2 (OR = 0.777, P < 0.05), suggesting resources negatively impacted membership in Class 2. Additionally, individuals with higher educational attainment (OR = 2.090, P < 0.01), those who performed night shifts 1~2 times per month (OR = 2.226, P < 0.05), opportunities (OR = 1.113, P < 0.05), informal empowerment (OR = 1.095, P < 0.05), and self-efficacy (OR = 1.112, P < 0.01) showed increased odds of belonging to Class 3 compared to Class 1. Among these predictors, performing 1~2 night shifts per month had the strongest positive association with Class 3. Similarly, opportunities (OR = 1.236, P < 0.05), resources (OR = 1.360, P < 0.05), and self-efficacy (OR = 1.358, P < 0.01) increased the odds of assignment to Class 4 versus Class 1, with resource availability having the greatest positive influence.

Table 5.

Multinomial logistic regression analysis of variables associated with latent profile membership (N = 659)

Variables	Class2		Class3			Class4
	b	OR (95% CI)	b	OR (95% CI)		b	OR (95% CI)
Highest degree	-0.058	0.943(0.461, 1.930)	0.737**	2.090(1.440, 3.035)		0.406	1.502 (0.737, 3.058)
Monthly night shift frequency(ref: ≥7)
0	0.435	1.546(0.538, 4.445)	-0.081	0.922(0.529, 1.608)		-0.396	0.673(0.240, 1.888)
1 ~ 2	1.350	3.856(0.970, 15.339)	0.800*	2.226(1.014, 4.887)		0.449	1.568(0.409, 6.013)
3 ~ 4	-0.739	0.478(0.138, 1.655)	-0.395	0.674(0.383, 1.185)		-0.760	0.468(0.157, 1.391)
5 ~ 6	-0.109	0.897(0.293, 2.743)	0.371	1.449(0.831, 2.525)		0.597	1.817(0.642, 5.143)
Opportunities	-0.149	0.862 (0.694, 1.069)	0.107*	1.113(1.001, 1.238)		0.212*	1.236(1.028, 1.485)
Resources	-0.252*	0.777 (0.620, 0.975)	0.013	1.013(0.907, 1.132)		0.307*	1.360(1.116, 1.656)
Informal empowerment	-0.076	0.927(0.772, 1.114)	0.091*	1.095(1.001, 1.198)		-0.009	0.991(0.847, 1.159)
Self-efficacy	-0.082	0.921(0.845, 1.004)	0.106**	1.112(1.069, 1.156)		0.306**	1.358(1.268, 1.455)

Note.^* means p < 0.05; ^** means p < 0.01

Discussion

This study identified significant heterogeneity in career adaptability among CSNs, identifying four distinct latent profiles: “high adaptability” (10.5%), “good adaptability” (42.6%), “moderate adaptability” (41.2%), and “low adaptability” (5.7%). The overall mean career adaptability score (22.16 ± 4.30) was below the theoretical midpoint (30 points), indicating a generally low level of adaptability among nurses within this specialty. Factors significantly affecting latent profile membership included educational level, monthly night shift frequency, opportunities, resources, informal empowerment, and self-efficacy.

Career adaptability of CSNs is relatively low

The observed mean score for overall career adaptability (22.16 ± 4.30) indicates that CSNs scored below the midpoint of the measurement scale, suggesting they encounter considerable difficulties in managing their professional trajectories. This finding aligns closely with the complex and demanding nature of cardiovascular nursing practice in contemporary healthcare environments [39]. Furthermore, the adaptability scores identified here were comparatively lower than those reported in studies conducted by Xu [40] and Awad [16], likely reflecting differences in clinical settings and specialty-specific challenges. However, they were notably higher compared to new graduate nurses [13], indicating a certain degree of adaptation advancement through clinical experience. The escalating complexity of cardiovascular care, driven by rapid technological advancements, innovative technologies, and an expanding scope of practice– poses elevated competency demands on nurses [41]. Combined with the inherent stressors typical of cardiovascular settings, such as frequent emergencies, heightened emotional demands, sleep disturbances, irritability, and anxiety [42], these conditions may exhaust psychological resources critical for adaptive career responses.

Career adaptability of CSNs can be categorized into four distinct types with heterogeneity

High adaptability

Nurses classified within this subgroup typically reported not undertaking night shifts and exhibited good physical health status. Avoiding night shifts helps maintain nurses’ circadian rhythms, prevents sleep disturbances, and reduces associated fatigue [43, 44], thus promoting sustained well-being and professional performance. Good physical health further contributes essential energy reserves that support confidence in managing challenging responsibilities, foster ongoing professional development, and build resilience, key attributes underpinning career adaptability [45]. Nurses in this subgroup appear adept at navigating professional challenges, demonstrating strong adaptive preparedness and potentially corresponding to more advanced stages of career maturity as outlined in CDT.

Good adaptability

This subgroup represented the largest proportion, predominantly consisting of nurses possessing bachelor’s degrees and having fewer than five years of clinical experience. This profile suggests a developmental phase. Despite a solid theoretical foundation, transitioning from academic training to specialized cardiovascular nursing practice frequently involves substantial obstacles [46]. Limited clinical exposure may constrain the effective integration of theoretical knowledge into practical clinical decision-making [47], thereby reducing professional confidence and hindering the optimal utilization of adaptability resources. According to CDT, nurses within this subgroup likely remain in the establishment phase, actively building competence and developing professional identities. It is therefore critical to implement targeted interventions aimed at bridging gaps between theory and practice. Examples of such targeted initiatives include developing specialized competency-based curricula tailored specifically for cardiovascular nursing [3], utilizing simulation-based training for high-acuity scenarios to build confidence and decision-making, establishing structured mentorship programs pairing novices with experienced CSNs to provide guidance and support navigating the complexities of the specialty, and use case-based learning to enhance critical thinking skills [48].

Moderate adaptability

This subgroup predominantly comprised nurses with moderate physical health and diverse professional titles, ranging from junior to senior levels. The findings emphasize the intricate interactions among multiple determinants of career adaptability. Generally, higher professional titles, reflecting increased training and extensive clinical experience, are assumed to enhance adaptability resources [48].

However, moderate physical health status might considerably limit available energy and overall performance, consequently restricting adaptability despite career maturity [49]. Conversely, junior nurses in this subgroup, though potentially benefiting from better physical health, experience typical early-career uncertainties, such as anxiety about future career progression and lower professional confidence, which may explain their moderate adaptability scores. This subgroup thus highlights the mediating effect of situational variables, such as physical health, on the link between career stage and realized adaptability.

Low adaptability

This subgroup, characterized by nurses without administrative responsibilities, lower monthly incomes (< 5000 RMB), and employment in Class II hospitals, represents a particularly concerning profile. Structural and organizational constraints appear to severely impede the development of adaptability resources within this subgroup. Possible underlying causes align with factors previously identified as diminishing nurses’ professional confidence and perceived control, such as limited opportunities for career advancement, exclusion from decision-making processes (low informal empowerment), inadequate organizational support, and insufficient professional recognition [50, 51]. Practical interventions for this subgroup may include establishing transparent career ladders within hospitals to illustrate clear professional advancement opportunities; implementing shared governance structures to involve nurses in decision-making; reviewing compensation structures and benefits specifically targeting CSNs’ specialized competencies; and strengthening organizational support mechanisms (e.g., readily available counseling, workload management tools, peer support groups).

Factors influencing career adaptability among CSNs

Demographic factors

Regression analyses revealed that monthly frequency of night shifts (OR = 2.226) and educational attainment (OR = 2.090) were significant predictors for inclusion in the “moderate adaptability” subgroup. Nurses assigned 1~2 night shifts per month demonstrated an increased likelihood of belonging to this category. A moderate night-shift frequency enables nurses to accumulate sufficient clinical experience while mitigating adverse effects, such as physical exhaustion and psychological strain associated with excessive shift work [43]. Additionally, balanced night shift assignments promote effective stress recovery, enabling improved clinical responsiveness and strengthened team collaboration [52, 53]. Nurses with undergraduate education showed significantly higher odds of being classified into the “moderate adaptability” subgroup compared to those with secondary vocational or junior college education. This result is consistent with CDT, which suggests that higher levels of education foster greater professional confidence and adaptability. Undergraduate-prepared nurses typically possess a more comprehensive theoretical foundation and systematic skill set, enabling efficient acquisition and application of new clinical techniques and technologies [54]. Therefore, nursing administrators should prioritize optimizing shift scheduling and actively facilitate ongoing professional development programs to bolster nurses’ career adaptability.

Structural empowerment

Shared governance, a fundamental aspect of contemporary nursing practice, empowers nurses by involving them in administrative and clinical decision-making, thus improving employee engagement, patient safety, and quality of care [55]. From a theoretical perspective, structural empowerment emphasizes the critical role of organizational support in fostering nurses’ adaptability. This study identified opportunities, resource availability, and informal empowerment as influential factors affecting career adaptability.

Opportunities significantly predicted membership in both “good adaptability” (OR = 1.113) and “high adaptability” (OR = 1.236) subgroups, indicating that nurses who perceive greater professional opportunities within the workplace have higher adaptability. This finding aligns with previous findings suggesting that increased opportunities for professional development enhance nurses’ perceived competence, job-related confidence [56] and clinical autonomy [57]. From a CDT perspective, workplace opportunities represent essential developmental inputs, facilitating nurses’ acquisition of necessary skills, experiences, and professional identities, all of which are foundational for adaptively navigating career transitions and challenges. Thus, greater perceived opportunities may empower nurses to engage proactively in career-related adaptive behaviors, such as setting goals, exploring new roles, and investing in skill development, thereby fostering higher adaptability. Therefore, nursing administrators should actively expand opportunities for professional growth, such as providing specialized training programs, mentorship arrangements, and leadership roles, thus enabling nurses to acquire diverse experiences and advanced skills.

Resource availability significantly influenced membership in the “low adaptability” (OR = 0.777) and “high adaptability” (OR = 1.360) subgroups, but had no significant impact on the “good adaptability” subgroup. This finding is consistent with CDT’s assertion that resources are key drivers of career development. Resources provide CSNs with opportunities to manage career challenges and enhance professional skills, thereby promoting their progression toward higher levels of adaptability. The non-significant impact on “good adaptability” may suggest that the transition from “moderate” to “good” adaptability requires the synergistic effect of additional factors or that the quality and utilization of resources may be more critical, warranting further research. Therefore, nursing administrators should routinely assess resource allocation and related adaptability metrics to identify deficiencies and develop targeted strategies for organizational improvement [58].

Informal empowerment emerged as a significant predictor of “good adaptability” subgroup (OR = 1.095). This indicates that informal empowerment, such as peer support, shared knowledge, and collaborative problem-solving, may crucially foster moderate yet effective career adaptability among CSNs, likely by providing psychological resources and practical guidance to navigate challenges and adjust to career demands [59]. Notably, informal empowerment was not significantly associated with the “high adaptability” or “low adaptability” profiles. For the “low adaptability” group, informal empowerment may insufficiently address deeply ingrained personal or structural barriers hindering adaptability. Conversely, nurses in the “high adaptability” group may possess inherent personal resources or robust formal support systems, making informal empowerment less critical or redundant. Thus, informal empowerment represents valuable support for nurses achieving or maintaining “good adaptability” but is not decisive for extreme adaptability levels. This highlights the importance of cultivating a supportive work environment to promote informal empowerment and enhance career adaptability.

Self-efficacy

Self-efficacy significantly increased the likelihood of nurses being categorized into “good adaptability” (OR = 1.112) and “high adaptability” (OR = 1.358) subgroups, while demonstrating no significant effect on “low adaptability” subgroup. According to CDT, self-efficacy is a critical personal determinant influencing career choices, persistence, and performance, particularly in challenging contexts. The robust association with the highest adaptability profile exemplifies this, suggesting nurses with strong self-efficacy beliefs are better able to confidently navigate complex career demands [60]. To bolster self-efficacy and consequently career adaptability, targeted interventions such as simulation-based mastery learning [61], structured educational programs [62], informatics competency training [63], and specialized communication skill development [64] are recommended for nursing administrators.

Limitations

Despite efforts to maintain data integrity, this research has certain limitations. First, the purposive sampling method utilized, rather than random sampling, may constrain the generalizability of the findings. Future research should involve more diverse healthcare institutions to improve representativeness. Additionally, career adaptability assessments relied on self-report questionnaires, which might introduce bias. The limitations of LPA should also be considered. Although LPA simplifies data presentation and interpretation by categorizing individuals based on their most likely profile, participants may not strictly belong to only one category. Furthermore, while LPA helps identify associations between variables and adaptability profiles, it does not reveal the exact causal mechanisms behind these relationships.

Conclusions

In summary, distinct profiles of career adaptability were identified among CSNs, and the optimal four-profile model included high adaptability, good adaptability, moderate adaptability, and low adaptability. Approximately 53.1% of CSNs fell into the high and good adaptability profiles. These profiles differed significantly in terms of sociodemographic characteristics, structural empowerment, and self-efficacy. Thus, nurse administrators should prioritize tailored supports: providing continuing education for nurses with lower educational attainment, optimizing shift scheduling (e.g., by reducing excessive night shifts), ensuring opportunities and resources, fostering informal empowerment, and customizing self-efficacy interventions. Policymakers can leverage these findings to inform curricular reforms in specialist nursing education and to develop evidence-based recruitment and retention policies for CSNs. Future research should: (1) longitudinally track profile stability and career transitions, (2) design and evaluate targeted interventions, and (3) validate profiles in other specialist nursing or cross-cultural populations.

Author contributions

Yanling Du, YongHong Yang, and Linyuan Zhang designed the study. YongHong Yang and Shizhe He collected the data. Li Gao and Shujie Guo designed and developed a database for the study. Yanling Du, Chao Wu and Ziyue Gai carried out data analysis and produced the initial draft of the manuscript. YongHong Yang, Xiaolan Guo and Linyuan Zhang contributed to revising the manuscript. All authors have read and approved the final submitted manuscript.

Funding

This study was supported by the Xijing Innovation Research Institute Joint Fund Project (LHJJ24HL01), Shaanxi Province Key Research and Development Program Project (2024SF-YBXM-666), and the Air Force Medical University Rapid Response Research Project (2023KXKT018).

Data availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Declarations

Ethical approval

All study protocols adhered to the Karolinska Institute’s ethical standards and the Helsinki Declaration of 1964 and its subsequent amendments. In addition, this study was reviewed and approved by the Ethics Committee of the second hospital affiliated to Air Force Medical University(No. K202403-02), and participants gave consent to complete the online survey.

Disclosure

All authors have approved the manuscript for publication, and it is an original study that has never been published previously and it is not being considered for publication elsewhere. Yanling Du, Chao Wu and Ziyue Gai are cofirst authors. Linyuan Zhang, Xiaolan Guo and Yonghong Yang are co-corresponding authors.

Additional points

Implications for Nursing Management. Our results indicate a critical need to improve Chinese CSNs’ career adaptability and optimize the management methods. Interventions at the departmental and organizational levels are desired to strengthen the training and utilization of CSNs.

Competing interests

The authors declare no competing interests.