Construction of a competency index system for Community-Based Gerontological Nurse Specialists: a Delphi study

Aim

To construct a competency index system for Community-Based Gerontological Nurse Specialists.

Background

Global population aging has created urgent demand for specialized geriatric nursing competencies, particularly in community settings. While international competency frameworks exist, a standardized competency index system for community-based gerontological nurse specialists (CGNS) that aligns with Chinese healthcare realities and policy priorities is lacking.

Methods

A literature review and semi-structured interviews were conducted to develop an initial framework. A two-round Delphi survey was employed to build the index system for Community-Based Gerontological Nurse Specialists.

Results

The final validated competency index system included 4 first-level indicators (knowledge, skills, abilities, motivation/traits), 13 s-level indicators and 40 third-level indicators. The effective response rates of the two expert consultation rounds were 100%. The expert authority coefficients were 0.888. The coefficients of variation were 0.1192 and 0.0961, and the Kendall’s coefficient of concordance were 0.254 and 0.382, respectively (P < 0.05).

Conclusion

The competency index system of community gerontological specialist nurses constructed through the competency onion model is scientific and practical, which can provide a reference for the training, assessment and evaluation of senior community geriatric nursing talents.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12912-026-04489-z.

Implications for nursing management

This index system enables the development of standardized CGNS training curricula while facilitating competency-based performance evaluation, and also provides theoretical guidance for Master of Nursing Specialist(MNS) students of higher nursing colleges to enable them to undertake advanced community elderly care practices, thereby supporting the alignment with China’s ongoing primary care reforms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12912-026-04489-z.

Introduction

With the global population aging at an unprecedented rate, the demand for specialized healthcare services for older adults has surged. By the late 2070s, the number of persons aged 65 years and higher globally is projected to reach 2.2 billion [1], posing substantial challenges to health systems worldwide. Older adults are commonly affected by multiple chronic diseases, multimorbidity, and functional decline [2], making the traditional hospital-centered and episodic model of care insufficient to meet their long-term and continuous health needs. Consequently, community-based geriatric nursing has become a vital component of sustainable healthcare systems.

However, a widening gap has emerged between the rapidly increasing demand for geriatric care and the limited supply of highly qualified nursing professionals [3]. The Community-based Gerontological Nurse Specialist (CGNS), as an advanced practice nurse with competencies, such as geriatric assessment, chronic disease management, and health promotion, plays a crucial role in bridging the gap between the growing demand for and limited supply of geriatric care services [3–5]. Yet, in some countries—particularly in developing regions—there remains a lack of unified standards and clearly defined competency requirements for the education and role development of CGNS, resulting in inconsistent training systems and unclear role positioning [6–8]. To address this demand–competency gap, it is imperative to establish a scientific and systematic competency framework for CGNS, delineating their competencies, such as knowledge, skills, and professional attributes, etc., so as to deliver high-quality, person-centered care in community settings.

Internationally, several countries have developed geriatric nursing competency models tailored to their healthcare contexts. The American Nurses Association (ANA) emphasizes evidence-based practice, interdisciplinary collaboration, and health promotion, with core dimensions including chronic disease management and palliative care [9]. The Canadian Gerontological Nursing Association (CGNA) framework prioritizes cultural safety, advocacy, and dementia care, reflecting the country’s multicultural demographics [10]. In Australia, validated scales incorporate legal/ethical considerations and e-health literacy, addressing technological advancements in aged care [11]. In Japan, models focus on home-visiting nursing skills and community-team collaboration, aligning with its super-aged society’s emphasis on home-based care [12]. Furthermore, the World Health Organization’s Integrated Care for Older People (ICOPE) framework provides a person-centered approach to assessing and managing intrinsic capacity, which aligns with the holistic care required in community settings [1].

While these models share a multidimensional approach - encompassing clinical care, communication, health promotion, and lifelong learning [13] - their applicability depends heavily on local contexts. As the largest developing country in the world, China’s aging process exhibits the characteristics of “premature aging before prosperity”, “large base and rapid pace”, as well as imbalance in urban-rural development [14]. Moreover, in terms of per capita GDP, total medical and health resources, and the improvement of the social security system, there are still certain gaps compared with the aforementioned countries [15].

Nursing roles represent socially constructed behaviors reflecting professional expectations. Competency frameworks must align with these roles to ensure practical use in credentialing and performance evaluation. For example, the U.S. and Canada link competencies to advanced practice roles (e.g., nurse practitioners), while Japan integrates them into community health worker training [9, 12]. Direct adoption of foreign standards risks misalignment with local practices. Existing Chinese studies on geriatric nursing competencies predominantly target hospitalized older adults, focusing on acute care skills [16, 17]. While existing competency frameworks, such as the Gerontological Nursing Competence Scale (validated in tertiary hospitals) identify six core domains—geriatric care delivery, communication, health promotion, evidence-based practice, professionalism, and organizational skills [13]—they overlook community-specific challenges such as home-based chronic disease management and caregiver support [18, 19].

The Onion Model is usually used as a theoretical framework for constructing a competency indicator system [20–22], which is developed based on the Iceberg Model. Proposed by Richard Boyatzis in the 1980s, it visualizes competency as layers of an onion, with outer layers representing observable behaviors and inner layers reflecting intrinsic traits [23]. Onion Model can systematically integrate multidimensional requirements in community settings, ranging from explicit nursing knowledge and skills to internal role competencies, and even deep-seated empathy and professional mission, thereby ensuring the comprehensiveness and hierarchical nature of the indicator system. Therefore, this study aims to construct a competency indicator system for CGNS in China based on the Onion Model, providing a reference for the cultivation of high-quality professional talents in community geriatric care, nursing human resource planning, and alignment with international competency standards in the future.

Methods

Design

An initial set of competency evaluation indicators for CGNS was developed based on a comprehensive literature review and semi-structured interviews. Building upon the preliminary framework, a Delphi questionnaire was formulated, followed by two iterative rounds of expert consultation to establish consensus on the finalized indicators. This study is based on the Delphi studies in social and health sciences – recommendations for an interdisciplinary standardized reporting (DELPHISTAR) [24]. The study protocol was not prospectively registered.

Construction of an evaluation index system

The research team consisted of 8 members, including 2 experts in geriatric care education, 2 managers from community health centers, 1 community nurse and 1 geriatric specialist nurse, and 2 Master of Nursing Specialist(MNS) students. The research team initially constructed the competency model of CGNS through literature review, semi-structured interviews, and the preliminary research results of the research group [4].

By combining subject terms with free words, a systematic search was conducted in English and Chinese databases, including PubMed, Web of Science Core Collection, EBSCO Platform (including MEDLINE, CINAHL Ultimate, Academic Search Complete, Eric, GreenFILE, Teacher Reference Center), Proquest, ScienceDirect, Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP, Wanfang, China Biomedical Database, and Chinese Medical Journal Full-text Database up to October 2024 to retrieve the literature related to “gerontological nursing,” “geriatric nursing,” “community health nursing,” “public health nursing,” and “competency.” The full search strategy for each database is provided in Supplementary Material 1. Guided by the Onion Competency Model, relevant competency elements identified in the literature were systematically categorized into a three-tiered indicator system. The first-level indicators were named according to the core dimensions of the Onion Competency Model, while the second- and third-level indicators were derived through rigorous content analysis. This phase resulted in an initial framework comprising four first-level indicators, eleven second-level indicators, and forty-four third-level indicators.

Subsequently, purposive sampling was employed to recruit participants for semi-structured interviews, including nurse educators, nursing administrators, and community/geriatric nurse specialists from community hospitals and healthcare centers. Additionally, key stakeholders such as managers of community health centers, older adults, and their family members were interviewed to represent societal needs. Primary interview questions included: (1) What are your perspectives on the current state of specialized geriatric nurse training in China’s community settings? (2) In your opinion, what knowledge, skills, abilities and qualities should an expert in community-based elderly care possess? (3) What specific healthcare services do community nursing staff primarily provide for you? (4) Based on your current needs, what additional assistance would you expect from nursing personnel? (5) How satisfied are you with existing nursing services, and what potential improvements would you suggest? (Questions 3–5 were addressed to elderly community residents and their family members) Data analysis was conducted using content analysis, with data collection and preliminary analysis occurring concurrently during the interview phase. Through this process, the preliminary competency model was refined, ultimately yielding 4 first-level indicators, 13 s-level indicators, and 41 third-level indicators.

Identification of the advisory experts

The number of Delphi consultants should be controlled at 15–30 to avoid homogeneity of research objects [25]. From December 2024 to February 2025, the research group distributed questionnaires to 15 experts across community hospitals, medical universities and major tertiary hospitals for experts consultation via email invitation. They were identified through professional networks, academic associations, and snowball sampling. The inclusion criteria were as follows: (1) A minimum of 10 years of work experience in nursing management, nursing education, community nursing, or geriatric nursing; (2) Hold a bachelor’s degree or higher in nursing or a related field; (3) Possess an intermediate-level or higher professional title (e.g., Nurse Practitioner, Clinical Nurse Specialist, or equivalent); (4) Voluntary participation in the study. Exclusion Criteria: (1) Failing to complete the questionnaire; (2) Failing to fill in and return the questionnaire within the specified time.

The expert consultation questionnaire

The expert consultation questionnaire mainly included the following sections: (1) Introduction: This section provided the research background, objectives, significance, and instructions for completing the questionnaire. (2) Questionnaire Body: The specific content covered dimensions such as knowledge, skills, abilities, motivation and traits for consultation. A Likert 5-point scale was used to assess the importance of each indicator, with scores ranging from 1 (not important) to 5 (very important). Experts may provide additional comments or suggestions in the remarks column. (3) Basic Information Survey: This included demographic details such as age, gender, education level, profession, professional title, and years of work experience. (4) Self-Assessment Form: Experts were asked to evaluate their familiarity with the research topic and the basis of their judgments.

Consulting and feedback cycle

The Delphi process consisted of two rounds conducted in December 2024 and February 2025 respectively. The expert consultation forms were distributed either in print or via email, and reminders were sent in two weeks. After collecting the first round of questionnaires, the quantitative data (mean scores, coefficients of variation) and qualitative feedback were compiled, analyzed, and discussed by the research team. Adjustments and revisions were made to the competency model indicators accordingly. A summary report including revised indicators and anonymized comments was sent to experts in the second round. The consultation process concluded once expert opinions reached a high level of consensus. Criteria for item deletion: An indicator was removed if (1) over 70% of the experts recommended its deletion, and/or (2) the mean importance score was < 3.5 with a coefficient of variation (CV) ≥ 0.25. Other expert suggestions for modification were reviewed and finalized by the research team after discussion. The process was anonymous after each round.

Statistical analysis

The data were entered and analyzed using Excel 2021 and SPSS 25.0. The measurement data were presented as means and standard deviations, while the count data were expressed as frequency and percentage. The authority of experts serves as an indicator reflecting the reliability of their opinions on the competency model for community geriatric nurse specialists, which is represented by the Expert Authority Coefficient (Cr). A higher Cr value indicates greater reliability of the results. The magnitude of Cr depends on two factors: the basis of expert judgment (Ca) and their familiarity with the content (Cs), calculated using the formula Cr=(Ca + Cs)/2. A Cr ≥ 0.7 is considered acceptable, while Cr > 0.8 indicates higher credibility. The judgment basis for the consultation content includes theoretical knowledge, practical experience, reference to domestic and international data, and intuitive perception, with the influence degree of each judgment basis classified as high, medium, or low. The familiarity with the content is divided into five levels: unfamiliar, somewhat familiar, moderately familiar, quite familiar, and very familiar. The concentration of expert opinions is represented by mean scores and full-mark ratios, where higher mean scores and higher full-mark ratios indicate greater consensus among experts. The coordination degree of expert opinions is measured by the coefficient of variation (standard deviation/mean) and Kendall’s coefficient of concordance, with smaller coefficients of variation and larger Kendall’s coefficients indicating better agreement among experts. We used the analytic hierarchy process to calculate the importance of each item of the 2 round Delphi consultation. YAHP V12.12.8367 was used to establish a judgment matrix and calculate the weights of the indicators.

Results

General information of the experts

A total of 15 experts completed two rounds of consultation, all from tertiary hospitals or medical colleges. The experts’ ages ranged from 35 to 51 years (43.14 ± 4.31), with work experience ranging from 10 to 31 years (16.14 ± 6.30). All experts held bachelor’s degrees or higher, including 8 (53.33%) with doctoral degrees. Their primary work areas involved community/gerontological nursing practice (40.00%), community/gerontological nursing education/training (66.67%), community/gerontological nursing research (53.33%), and community/gerontological nursing management (33.33%). General information about the experts is presented in Table 1.

Table 1.

General information of the experts (n = 15)

Characteristics	n (%)
Age(years)
30~	4(26.67)
40~	10(66.67)
50~	1(6.67)
Education background
Bachelor	2(13.33)
Master	5(33.33)
Doctor	8(53.33)
Professional title
Intermediate	3(20.00)
Associate senior	8(53.33)
Senior	4(26.67)
Research field
Community/Gerontological Nursing Practice	6(40.00)
Community/Gerontological Nursing Education & Training	10(66.67)
Community/Gerontological Nursing Research	8(53.33)
Community/Gerontological Nursing Management	5(33.33)

The degree of experts’ activeness and authority

For both rounds of expert consultation, 15 questionnaires were distributed and 15 were returned, achieving a 100% response rate. In each round, 12 (80.00%) and 11 (73.33%) experts provided feedback respectively, demonstrating high participation enthusiasm. Both rounds demonstrated high expert authority (Cr = 0.888, Cs = 0.862, Ca = 0.914).

The degree of coordination of experts’ opinions

For the first round of expert consultation, the coefficients of variation(CV) ranged from 0.0000-0.2548(the mean CV was 0.1192), with Kendall’s coefficient of concordance of 0.254 and χ² value of 232.542 (P < 0.001). In the second round, the coefficients of variation ranged from 0.0000 -0.2477(the mean CV was 0.0961), with Kendall’s coefficient of concordance of 0.382 and χ² value of 320.479 (P < 0.001), indicating good coordination of expert opinions. The Kendall harmony coefficient of all indexes in the second round of consultation is greater than that in the first round of consultation, which shows that the expert opinions in the two rounds of consultation tend to be unified. These findings are shown in Table 2.

Table 2.

The results of the degree of coordination of expert opinions

Item	The number of indexes	Kendall’s W	χ²	P
First round
Total	62	0.254	232.542	<0.001
First-level index	4	0.199	8.951	0.0299
Second-level index	14	0.175	34.212	0.0011
Third-level index	44	0.193	124.257	<0.001
Second round
Total	57	0.382	320.479	<0.001
First-level index	4	0.212	9.55	0.0228
Second-level index	13	0.23	41.488	<0.001
Third-level index	40	0.201	117.773	<0.001

The degree of concentration of experts’ opinions

In the first round of consultation, the mean importance scores for each item ranged from 4.13 to 5.00, with standard deviations ranging from 0.00 to 1.12. In the second round, the mean importance scores ranged from 4.47 to 5.00, with standard deviations ranging from 0.00 to 0.83. The importance scores and coefficients of variation for each item in the second round are shown in Table 3.

Table 3.

Consultation results of indicators at all levels of the index system for Community-Based Gerontological Nurse Specialists

The results of the expert consultation

Results of the first round consultation

After the first round of expert consultation, based on the indicator screening criteria, expert opinions, and research group discussions, a total of 4 items were added, 9 items were deleted, and 7 items were modified. These included: (1) modification of one first-level indicator, changing “Community Geriatric Professional Care Skills Dimension” to “Skills”; (2) deletion of one second-level indicator(Nursing research knowledge), modification of five second-level indicators, changing “Community Geriatric Care and Care Professional Knowledge” to “Community Geriatric Specialized Nursing Knowledge”, changing “Community Nursing and Management Knowledge” to “Community Nursing Management Knowledge”, changing “Humanistic Knowledge” to “Community Geriatric Nursing Humanistic Knowledge”, changing “Community Geriatric Health Assessment/Plan” to “Community Geriatric Nursing Program Application Skills”, and changing “Community Elderly Health Promotion Ability” to “Community Geriatric Health Education and Health Promotion Ability”; and (3) modification of one third-level indicator(change “Personalized care plan and implementation” to “Personalized care plan, implementation and evaluation”), deletion of eight third-level indicators (“Information and technology”, “basic first aid knowledge”, “cutting-edge knowledge in community elderly care”, “Health Monitoring Ability”, “Risk Prevention and Control Ability”, “Emergency Response Ability”, “Emotional Support Ability”, “Decision-Making Ability”), and addition of four new items (“Aging-Related Knowledge”, “Rehabilitation Knowledge”, “Palliative Care Knowledge”, “Ethical decision-making ability”).

Results of the second round consultation

In the second round of consultation, expert opinions on the indicators were largely consistent, with no constructive suggestions for deletion or addition of items, only recommendations for wording adjustments in some items. All items met the preset statistical criteria, and after aggregating the indicator weight values, the central tendency of expert opinions was favorable, leading to the termination of consultation. Based on the opinions and suggestions from the two rounds of expert consultation, a final competency evaluation index system for CGNS was established, consisting of 4 first-level indicators, 13 s-level indicators, and 40 third-level indicators (Fig. 1), with corresponding weight values determined for each indicator. We used hierarchical analysis to calculate the weights of the indicators and constructed a judgment matrix based on the average of the importance assignments. All CR values are < 0.1, and the judgment matrix in this study meets the consistency test (Table 3).

Fig. 1.

Consultation process flowchart

Discussion

Guided by the Onion Model, this study developed an indicator system comprising four concentric layers (from outer to inner: knowledge, skills, abilities, and motivations & traits). This was achieved through a systematic literature review, semi-structured interviews, and the research team’s preliminary findings [4]. Finally, a Delphi study was conducted to refine the framework, resulting in 4 first-level, 13 s-level, and 40 third-level indicators.

The Delphi method employed in this study demonstrated high reliability, as evidenced by the expert response rates of 100% in both rounds and the high authority coefficient (Cr = 0.888). These findings with response rates above 70% and authority coefficients exceeding 0.7 are considered indicative of strong consensus and reliability. The experts’ substantial clinical and academic experience (mean work experience = 16.14 ± 6.30 years) ensured the content validity of the indicators. The Kendall’s coefficient of concordance of 0.254 (first round) and 0.382 (second round) with significant chi-square tests (P < 0.001) indicate good coordination among experts. The decreasing variation coefficients from the first to second round (0.1192 to 0.0961) demonstrate increasing consensus. This methodological rigor supports the validity of the developed index system.

Some indicators may appear overlapping, such as “Geriatric Nursing Theory” (III-2) and “Rehabilitation Knowledge” (III-6). However, the former refers to foundational principles of aging and care models, while the latter pertains to applied knowledge for functional recovery. Similarly, “Chronic Disease Management” (III-15) focuses on long-term condition control, whereas “Rehabilitation Nursing” (III-16) emphasizes restorative interventions. These distinctions reflect different aspects of community geriatric care.

Among the first-level indicators, abilities carried the highest weight (weight = 0.4460), reflecting a shift in the CGNS competency index system from knowledge or technical skills toward comprehensive, context-adaptive capabilities. Within this dimension, health promotion and education abilities in community health care (weight = 0.1346), interpersonal abilities (weight = 0.0968), and critical thinking abilities (weight = 0.0957) ranked highest among secondary indicators, consistent with internationally recognized frameworks such as the International Council of Nurses (ICN) competency framework, which also emphasizes health education, communication, and critical thinking [26]. At the third level, health literacy promotion (weight = 0.0740), communication (weight = 0.0774), and evidence-based practice (weight = 0.0460) further highlight the centrality of abilities. These findings suggest CGNS should demonstrate three core attributes: establishing effective nurse–patient relationships through communication; designing and delivering health education and literacy promotion to support self-management; and applying evidence, clinical expertise, and patient preferences to guide person-centered decisions.

The dimension of motivation and traits accounted for a weight of 0.2601, ranking second among first-level indicators, highlighting that CGNS competency involves not only “what they can do” but also “why they do it” and “whether they can sustain performance.” According to the Onion Model of Competence, motivation and traits are core drivers of professional development and service quality, closely tied to job performance and career sustainability [23]. Among second-level indicators, motivation (weight = 0.1856) was most emphasized, with creating social value (weight = 0.1237) and personal career development (weight = 0.0619) emerging at the third level. The prominence of social value realization reflects community geriatric nursing’s social mission—reducing family burdens, improving elder health, and aligning with Healthy China 2030 and national aging strategies [27]. Meanwhile, personal career development (weight = 0.0619) underscores nurses’ commitment to continuous learning and adaptability amid population aging. While the assessment of these indicators is inherently more complex than that of knowledge-based indicators, practical approaches can be utilized. These may include multi-source feedback (e.g., from patients, peers, and supervisors), structured behavioral observations during simulated or real clinical encounters, and reflective practice portfolios. Correspondingly, professional development may involve mentorship, scenario-based training, and reflective practice.

The knowledge dimension (weight = 0.1735) and skills dimension (weight = 0.1203) dimensions, though weighted lower, remain essential to the CGNS competency framework. This reflects that while motivation and abilities are primary drivers, solid knowledge and technical proficiency underpin effective practice. Unlike hospital-based geriatric nursing [16], which emphasizes advanced technical skills, the weighting highlights the distinct requirements of community care. Among second-level indicators, legal and ethical knowledge in community geriatric nursing (weight = 0.0175) carried the lowest weight, yet its inclusion affirms the importance of legal–ethical literacy in geriatric practice [16]. At the third level, emergency care for common geriatric acute illness/accidents (weight = 0.005) ranked lowest, as these are mainly managed in hospitals; however, community nurses’ role in early identification and timely referral remains vital, making this a “hidden pillar” of the framework.

While several studies have developed competency frameworks for geriatric nurses [10, 16] this study uniquely focuses on the community setting. For example, this study highlights the distinctive features of the competency framework for community geriatric specialist nurses. Among the third-level indicators, public health knowledge (weight = 0.0358), community-based chronic disease management(weight = 0.0327), transitional care(weight = 0.0323), and caregiver support(weight = 0.0282) ranked highly, underscoring the importance of prevention, health promotion, and long-term management in community practice. Unlike hospital-based geriatric nurses who focus on acute treatment and rehabilitation, community nurses must integrate individual care with population-level health education and chronic disease prevention to reduce hospitalizations. The prominence of continuity of care and caregiver support further reflects the need for home-based services and family empowerment. Although health data analysis (weight = 0.0150), health records management (weight = 0.0075), and community management knowledge (weight = 0.0090) carried lower weights, they still indicate the growing importance of digitalization and resource integration. The inclusion of cross-cultural nursing (weight = 0.0194) highlights the need for effective practice in diverse, multidisciplinary settings. Overall, community geriatric specialist nurses require a broader, integrative skill set centered on “prevention, education, long-term management, information integration, and family support,” aligning with the Healthy China 2030 initiative [27].

The developed competency index system offers some avenues for practical application. It serves as a foundational guide for designing both pre-service and continuing education curricula for CGNS, ensuring that training aligns closely with requisite competencies. Furthermore, the framework can inform the establishment of certification and credentialing standards for community geriatric nurse specialists. In clinical and managerial contexts, healthcare organizations may utilize these indicators to develop structured, competency-based tools for performance evaluation. In conclusion, the developed index system addresses a critical need in China’s healthcare system, where standardized competency frameworks for community geriatric nurses have been lacking [28, 29]. This competency framework’s hierarchical structure and clearly defined indicators serve as a valuable tool with multiple practical applications. It provides a guidance for developing targeted training curricula, particularly for nurses specializing in community-based gerontological nursing [30, 31]. The framework also enables standardized performance evaluations for community-based geriatric nurses, where such assessment tools have previously been lacking [32, 33].

Limitations and future research

While we established a relatively comprehensive competency framework through this study, several limitations should be acknowledged. The panel consisted solely of nursing professionals; future studies should include perspectives from older adults, family caregivers, and interdisciplinary team members to enhance relevance. Although we implemented rigorous Delphi procedures, the framework still requires validation through practical implementation studies. Additionally, regional variations across China may necessitate cultural adaptations when applying this framework in different areas. This focused recruitment was intentional to ensure the framework’s relevance to the Chinese healthcare context, but it inherently limits the index’s generalizability to other countries with different cultural norms and health systems. Cultural factors specific to China—such as the central role of family in elder care (filial piety), particular communication styles with older adults, and societal expectations of nurses—likely influenced which competencies were emphasized (e.g., family caregiver support, communication tailored within this dynamic). Similarly, systemic factors like the structure of China’s community health services, nurses’ scope of practice, and available resources shape the perceived feasibility and importance of certain competencies. Therefore, while this framework provides a valuable foundation for China, its direct application internationally requires careful contextual adaptation and validation.We have taken steps to enhance the framework’s validity, but further research efforts are needed. Future studies could focus on validating the framework through empirical research involving community geriatric nurses. The development of standardized assessment tools based on these indicators would also be valuable for practical application. Furthermore, investigating the actual impact of specific competencies on patient outcomes could provide crucial evidence for implementation [20, 34].

Conclusions

Using the Delphi method, this study established a valid competency index system for CGNS, which includes 4 first-level indicators, 13 s-level indicators and 40 third-level indicators. The competency index system is helpful to standardize the practice of community gerontological nursing and provide guidance for the training, assessment and evaluation of senior community geriatric nursing talents. It can also provide a reference for other developing countries to build a localized competency index system for community geriatric specialist nurses, improve the quality of community geriatric nursing personnel training through the implementation of this framework, and support the professional development of community geriatric nurses.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

HFZ responsible for research conception, research implementation, article writing and revision. XLL responsible for article writing and data analysis. XYL responsible for article writing and data analysis. BJC responsible for questionnaire distribution, data collation and statistical analysis. RH responsible for project supervision and manuscript review. HL responsible for overall project management, project supervision, expert recruitment and manuscript review. All authors have read and agreed to the final version of the manuscript.

Funding

This study was supported by the 2023 Education and Teaching Reform Research Project for Undergraduate Colleges and Universities in Fujian Province—Key Project (Postgraduate Category, Grant No. FBJY20230108), and the 2023 Postgraduate Education and Teaching Research Project of Fujian Medical University (Grant No. Y23001).

Data availability

The datasets generated and analyzed during the current study are not publicly available due to the protection of the privacy of consulting experts but are available from the corresponding author (leehong99@126.com) on reasonable request.

Declarations

Ethical approval and consent to participate

The study was performed in accordance with the Declaration of Helsinki and had been reviewed by the Ethics Committee of Fujian Medical University, Fuzhou, China and met the requirements for exemption from ethics review. Written informed consent was obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.