Developing competency evaluation indicators for neurosurgical specialist nurses: a Delphi-AHP study

Abstract

Background

Neurosurgical nursing is a high-demand specialized field requiring comprehensive professional competency. In 2023 when this study commenced, no standardized, validated competency evaluation framework existed for Chinese neurosurgical specialist nurses, and no official certification program was available. Although the Chinese Nursing Association launched neurosurgical specialist nurse certification in 2025, a structured, evidence-based assessment tool remains lacking, leaving a critical gap in clinical nursing talent training and management. This study aimed to construct competency evaluation indicators for neurosurgical specialist nurses.

Methods

A mixed-methods Delphi-AHP research design was adopted. Based on systematic literature retrieval, theoretical research, and group discussions, the initial draft of Competency for Neurosurgical Specialist Nurses was preliminarily formulated. A systematic literature review was conducted in 5 domestic and foreign databases from 2018 to 2023, with clear inclusion and exclusion criteria; 15 experts with rich neurosurgical nursing experience were selected for expert consultation. Subsequently, two rounds of expert consultations using the Delphi method were conducted to modify, add, and delete indicators, and the analytic hierarchy process (AHP) was used to determine the weight of each indicator.

Results

The effective questionnaire response rates for both rounds of expert consultations were both 100%. The expert authority coefficients of the two rounds were 0.907 and 0.913, respectively. The Kendall’s concordance coefficients for the two rounds were 0.164 and 0.343,< 0.01). Finally, a set of competency evaluation indicators for neurosurgical specialist nurses was developed, including 5 first-level indicators, 14 s-level indicators, and 64 third-level indicators; among them, Professional Theoretical Knowledge and Professional Skills had the highest comprehensive weights (both 0.3717).

Conclusion

The competency evaluation indicators for neurosurgical specialist nurses constructed in this study show good expert authority, high consensus and acceptable methodological validity verified by Delphi and AHP methods, provide a structured reference for training, assessment, and management of neurosurgical specialist nurses.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12912-026-04952-x.

Introduction

With the rapid development of medical technologies and equipment, in the field of modern medicine, neurosurgery, as a highly specialised discipline [1], focuses on the treatment of brain trauma, brain tumors, cerebrovascular diseases, and functional disorders. These diseases are not only complex and changeable but also directly related to the quality of life and survival status of patients. Neurosurgical operations are also characterized by high risks and numerous complications, which greatly increase the workload and difficulty of nursing [2], so neurosurgery is also known as the “pinnacle of surgery”. As a solid defense line for protecting patients’ brain health, the nursing of neurosurgical diseases is not a simple auxiliary process, but a core component deeply embedded in the medical system. Neurosurgery requires advanced specialized nursing, and only nurses who possess professional skills are qualified to engage in this work, which is also called post competency [3]. It refers to the sum of knowledge, skills, and personal attributes that enable employees to be competent in their positions and achieve excellent performance within a specific organization.

Despite increasing clinical demands, no standardized competency framework exists for neurosurgical specialist nurses in China (as of 2023), with formal certification only initiated by the Chinese Nursing Association in 2025. Existing relevant studies at home and abroad mostly focus on single-dimensional competency evaluation or partial nursing skills, lacking a systematic, hierarchical and weighted evaluation system, and cannot fully meet the needs of clinical neurosurgical nursing practice and talent certification. This prospective study was conducted in 2023 aiming to fill the gap of specialized nursing competency evaluation before the official launch of national certification, and develop a scientific tool to adapt to the subsequent standardized training and certification work.

Neurosurgical nurses not only need to master specialized knowledge and operational skills proficiently, but also need to keep up with the cutting-edge of medical technology, actively learning and exploring the research hotspots and frontiers in this field. By applying scientific nursing methods and exquisite specialized nursing techniques, they aim to reduce nursing risks and improve nursing quality [4]. This study aims to scientifically construct the job competency items and indicators for neurosurgical specialist nurses through systematic research methods, so as to provide a basis for the development of neurosurgical specialized nursing talents. In China, advanced neurosurgical nursing practice is still in the development stage, and there is a lack of clear positioning for specialist nurses’ competency requirements in clinical practice; the Beijing Nursing Association has not yet launched a certification program for neurosurgical specialist nurses, and only has certification systems for a few other nursing specialties. Therefore, this study aimed to develop a structured competency evaluation framework for neurosurgical specialist nurses to support training, assessment, and management in clinical practice, especially for clinical neurosurgical nurses (including neurosurgical ICU nurses) with bachelor’s degree and above, to meet the competency needs of clinical frontline and critical care nursing.

Research methods

Establishment of the research team

The research team collaborated with 10 members, including 1 Associate Chief Nurse (Head Nurse of Neurosurgery, with more than 15 years of neurosurgical nursing management experience), 1 Chief Physician (Director of Neurosurgery, with more than 20 years of neurosurgical clinical work experience), 2 resident physicians (with more than 5 years of neurosurgical clinical practice experience), 2 charge nurses (stroke specialist nurses, with more than 10 years of specialized nursing work experience), and 4 clinical nurses (with more than 5 years of neurosurgical clinical nursing experience). Team members collaborated with divided responsibilities and the main tasks as follows: (1) retrieving, reading, and analyzing literature; (2) sorting and analyzing the indicator contents from included literatures to preliminarily determine the job competency indicators for neurosurgical specialist nurses; (3) compiling expert Delphi questionnaires; (4) selecting expert consultants; (5) organizing and analyzing suggestions from each round of expert questionnaire responses.

Formulating the initial draft of indicators

Literature retrieval

An extensive systematic review of domestic and foreign literature was conducted. Databases including CNKI, WanFang, VIP, PubMed, and Web of Science were retrieved; the search time range was from January 2018 to December 2023. The English search terms were “neurosurgery” AND “specialist nurse or neurology nurse” AND “nursing competence or expert ability or nursing ability”. The Chinese search terms were “神经外科” (neurosurgery), “专科护士” (specialist nurse) or “神经科护士” (neurology nurse), “岗位胜任力” (post competency), “专家能力” (expert ability) or “护理能力” (nursing ability).

Inclusion criteria: full-text studies closely related to neurosurgical specialist nurse competency; studies with clear and standardized research methods. Exclusion criteria: review articles, case reports, duplicate published literature, and studies irrelevant to the research topic. A total of 82 articles were initially retrieved, and 27 articles were finally included after screening. (A PRISMA flowchart of literature screening is shown in Supplementary Fig. 1)

Group discussion method

The discussion about the “evaluation indicators for job competency of neurosurgical specialist nurses” was based on the aims for training neurosurgical nurses, nurse competencies, institutional norms and international advanced neurosurgical nursing competency guidelines in the world, as well as the current development status of neurosurgical specialized nursing in China. Three structured group discussions (90 min each) were held, all discussions were recorded and transcribed verbatim, and the content was systematically sorted and analyzed by two team members independently. The included indicators were sorted and analyzed through comprehensive literature research, an item pool for the evaluation system of job competency for neurosurgical specialist nurses was preliminarily formed.

Implementation of the Delphi method

Compilation of expert consultation questionnaire

The Delphi questionnaire used in this study was developed by the research team following an extensive review of domestic and international literature, relevant competency frameworks, and group discussions. The first-round expert consultation questionnaire is provided as Supplementary File 1.

The expert consultation questionnaire mainly included the following four aspects:

1. Questionnaire instructions: briefly introducing the purpose, content, significance, and precautions of this study.

2. Questionnaire on basic information of experts, including age, education background, professional title, working years, engaged field etc.

3. Questionnaire on experts’ familiarity with this study and judgment basis. Familiarity was assigned values of 0.2, 0.4, 0.6, 0.8, and 1.0 for “very unfamiliar, less familiar, generally familiar, more familiar, very familiar”; the assignment of judgment basis is shown in Table 1. The assignment standard of expert judgment basis was formulated with reference to the mature scale development research norms of nursing discipline [5].

Table 1.

Assignment table of expert judgment basis

Judgment basis	Expert self-evaluation
	Large	Medium	Small
Theoretical analysis	0.3	0.2	0.1
Work experience	0.4	0.3	0.2
Bibliography	0.2	0.1	0.1
Intuitive choice	0.1	0.1	0.1

Selection of consulting experts

According to the research objectives and the characteristics/requirements of the Delphi method [5], consulting experts were determined. After informed consent of all experts, the criteria for selecting expert panelists was as follows:

1. Engaged in neurosurgery or related fields for more than 10 years;

2. Holding an intermediate or senior professional title;

3. Having a bachelor’s degree or above;

A total of 15 experts from 15 tertiary hospitals in China were included, and all 15 experts participated in two rounds of consultation.

Implementation of expert consultation

The first round of Delphi consultation was carried out from December 20 to 27, 2023; the second round was conducted from January 3 to 10, 2024. The researchers distributed Delphi questionnaires via email or paper copies. Each round of expert consultation lasted 7 days. Before sending the questionnaires, researchers contacted experts to explain the research objectives and content, and promptly addressed their questions. After the first round of expert consultation, two members of the team verified the completeness of information and input data to ensure accurate aggregation of expert opinions. All items were sorted, analyzed, summarized, and discussed, followed by addition/deletion and modification of corresponding indicators, which were then checked by research team members. The second round of expert consultation was conducted in the same manner, and the consultation was terminated when expert opinions achieved consensus.

Indicator screening

Based on the inclusion criteria for indicator screening [6], the screening criteria were set as: mean importance score > 3.5 and coefficient of < 0.25.

In the first round of consultation, 4 indicators were deleted, 17 indicators were added, and 15 indicators were modified; in the second round, only 1 indicator was modified. (Round-by-round indicator changes are shown in Supplementary Table 1)

Statistical methods

Excel 2019 and SPSS 24.0 statistical software were used for data entry, processing, and analysis. For categorical data, frequencies and percentages were adopted; for measurement data, means and standard deviations (Mean ± SD) were applied. The concentration degree of expert opinions was expressed by the mean value of importance scores and full-mark rate. The expert authority was measured by the authority coefficient, calculated as (expert familiarity + expert judgment basis)/2. The expert engagement degree was represented by the effective recovery rate (%) of consultation questionnaires, calculated as (number of recovered questionnaires / number of distributed questionnaires) × 100%, and a recovery rate > 70% was considered valid [7]. An expert authority coefficient ≥ 0.7 indicated the research results were reliable [8]. The coordination degree of expert opinions was expressed by Kendall’s W coefficient and coefficient of variation (CV). Kendall’s W ranges from 0 to 1, where a larger value indicates better coordination. A CV less than 0.25 is within the acceptable range, and a smaller CV value represents better coordination [9].

Although the Kendall’s W coefficient of the first round was 0.164 (poor consensus), all indicators basically met the screening criteria, and experts put forward a large number of constructive modification opinions, which could effectively optimize the indicator system, so it was necessary to carry out the second round of consultation. After the second round, Kendall’s W coefficient increased to 0.343 (moderate consensus), and all CV values were less than 0.25, indicating that expert opinions had reached a consistent level, so the consultation was terminated after 2 rounds.

The analytic hierarchy process (AHP) was applied to assign Saaty scale values based on the mean importance scores of each indicator. Pairwise comparison matrices were constructed for indicators within the same hierarchical level (the pairwise comparison matrix of each level is shown in Supplementary Table 2). This process yielded the relative weights, comprehensive weights, and consistency ratios (CR) for all indicators [10]. The CR threshold of AHP was set as < 0.1, indicating that the consistency of the judgment matrix was acceptable.

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki.

The study protocol was reviewed and approved by the Ethics Committee of Wangjing Hospital, China Academy of Chinese Medical Sciences.

Informed consent was obtained from all experts who participated in the Delphi survey.

All research data were stored in encrypted computers, and expert personal information was anonymized during data analysis and manuscript writing to ensure data security and expert anonymity.

Results

Basic information of experts

A total of 15 nursing experts from 15 tertiary and above hospitals were selected in this study, involving clinical nursing management, nursing education and other fields. The mean age of experts was 43.27 ± 4.56 years old, and the mean working years in neurosurgical nursing was 20.13 ± 5.24 years. The basic information of the experts is shown in Table 2.

Table 2.

Basic information of experts

Category	Grouping	Frequency(person)	Percentage(%)
Age	30–40	4	26.67
	41–50	7	46.67
	≥ 51	4	26.67
Professional Title	Charge Nurse	10	66.67
	Associate Chief Nurse	5	33.33
Working Years	10–20	6	40.00
	21–30	5	33.33
	≥ 31	4	26.66

Expert engagement

15 questionnaires were distributed in each of the two rounds of Delphi consultation, with 15 valid questionnaires were recovered in each round. The effective recovery rate of questionnaires was 100% in both rounds, which was higher than 70%, indicating that experts had a high enthusiasm for this study. In the first round, 10 experts (66.67%) put forward opinions, and in the second round, 4 experts (26.67%) put forward opinions.

Authority level (Table 3)

Table 3.

Expert authority level

Round	Judgment Coefficient (Ca)	Familiarity Coefficient (Cs)	Authority Coefficient (Cr)
1	0.88	0.933	0.907
2	0.893	0.933	0.913

The expert authority coefficients of the first and second rounds were 0.907 and 0.913 respectively. Both authority coefficients were greater than 0.7, indicating that the experts consulted in this study had high authority. The familiarity coefficient (Cs) was identical across the two Delphi rounds because the same panel of experts participated, and their self-assessed familiarity with the research topic remained unchanged.

Degree of expert opinion coordination (Table 4)

Table 4.

Degree of expert opinion coordination

Item	First Round Delphi Survey			Second Round Delphi Survey
	W-value	X2value	P-val	W-value	X2value	P-val
Level-1 Indicators	0.349	20.952	< 0.01	0.629	37.765	< 0.01
Level-2 Indicators	0.246	33.169	< 0.01	0.404	78.776	< 0.01
Level-3 Indicators	0.133	99.868	< 0.01	0.171	161.916	< 0.01
Overall	0.164	159.97	< 0.01	0.343	421.99	< 0.01

The range of the coefficient of variation (CV) for the indicators in the two rounds of the Delphi survey was 0.0000–0.2712 and 0.0000–0.2019, respectively. After both rounds, the coefficient of variation for all indicators was below 0.25, suggesting there was a good coordination among experts even though regarding the various indicators. The Kendall’s W values for the two expert consultation rounds were 0.164 and 0.343, respectively, with p-values less than 0.01, indicating that the experts’ opinions were reaching a consistent consensus.

Results of the first round delphi survey

In the first round, a total of 10 experts provided constructive suggestions. Based on the indicator screening criteria, expert opinions, and group meeting discussions, the indicators were revised as follows:

• Deletion of 4 indicators: Level-3 indicators such as “Collection of Arterial Blood Specimens” and “Transfusion Techniques and Management of Common Complications” were deleted.

• Addition of 17 indicators:4 Level-2 indicators, such as “Neurocritical Care Nursing” and “Effective Coordination Ability,” were added. 17 Level-3 indicators, such as “Muscle Strength and Muscle Tone Assessment” and “Prevention and Nursing Measures for Deep Vein Thrombosis,” were added.

• Modification of 15 indicators: For example, “Prevention and Nursing Measures for Pressure Ulcers” was modified into “Nursing Measures and Identification of Pressure Injuries,” and “Emergency Response Capability for Grand Mal Seizures” was modified into “Emergency Response Capability for Seizures and Status Epilepticus.”

Results of the second round delphi survey

Following the second round of expert consultation, based on the indicator screening criteria, expert opinions, and practical considerations discussed by the group, one indicator was modified: “Use and Maintenance of Emergency Equipment” was revised into “Application Capability of Emergency Equipment.”

This finalized the Neurosurgery Specialist Nurse Job Competency Evaluation Index System (Table 5), consisting of: 5 first-level indicators, 14 s-level indicators, 64 third-level indicators. The 5 first-level indicators are: Professional Theoretical Knowledge, Professional Skills, Communication and Coordination Ability, Research and Teaching Ability, Professional Qualities and Ethics.

Table 5.

Neurosurgery specialist nurse post competency evaluation index system

Indicator	Importance Score (Points, X ± s)	Full Score Rate (%)	Coefficient of Variation	Relative Weight	Comprehensive Weight	CR Value
A Professional Theoretical Knowledge	5.0000	1	0	0.3717	0.3717	< 0.001
A1 Neurosurgery-Related Theoretical Knowledge	5.0000	1	0	0.3333	01239	0.0217
A11 Neurological System Anatomy Structure and Function	4.8667 ± 0.3519	0.8667	0.0723	0.1653	0.0205
A12 Basic Knowledge of Common Neurosurgical Diseases	5.0000	1	0	0.3502	0.0434
A13 Ability to Identify Abnormal Laboratory Values	4.8667 ± 0.3519	0.8667	0.0723	0.1653	0.0205
A14 Ability to Interpret Imaging Examination Results	4.4667 ± 0.5164	0.4667	0.1156	0.0686	0.0085
A15 Neurosurgical Medication Knowledge	4.9333 ± 0.2582	0.9333	0.0523	0.2506	0.0310
A2 Neurological System Assessment Knowledge	5.0000	1	0	0.3333	0.1239	0.0173
A21 Muscle Strength and Muscle Tone Assessment	5.0000	1	0	0.3311	0.0410
A22 Ocular Function Assessment	5.0000	1	0	0.3311	0.0410
A23 Sensory Function Examination	4.6 ± 0.7368	0.7333	0.1602	0.0961	0.0119
A24 Neurological Reflex Examination	4.6 ± 0.7368	0.7333	0.1602	0.0961	0.0119
A25 Cognitive Function Examination	4.7333 ± 0.7037	0.8667	0.1487	0.1456	0.0180
A3 Application of Common Neurological System Assessment Scales	5.0000	1	0	0.3333	0.1239	0.0173
A31 Glasgow Coma Scale (GCS)	5.0000	1	0	0.2996	0.0371
A32 Pain Scale	5.0000	1	0	0.2996	0.0371
A33 Barthel Index Assessment Scale	4.9333 ± 0.2582	0.9333	0.0523	0.1721	0.0213
A34 Anxiety and Depression Scales	4.4 ± 0.8281	0.6	0.1882	0.0568	0.0070
A35 Insulin Clinical Test Quality Assessment Scale	4.9333 ± 0.2582	0.9333	0.0523	0.1721	0.0213
B Professional Skills	5.0000	1	0	0.3717	0.3717	< 0.001
B1 Neurosurgery Nursing Routines	5.0000	1	0	0.25	0.0929	0.0139
B11 Admission/Discharge, Transfer In/Out Handover Process	4.8 ± 0.5606	0.8667	0.1168	0.0562	0.0052
B12 Ability to Monitor Neurosurgical Patient Condition (e.g., state of consciousness, pupil observation)	5.0000	1	0	0.1783	0.0166
B13 Ability to Manage Central Hyperthermia in Neurosurgical Patients	4.8667 ± 0.3519	0.8667	0.0723	0.0891	0.0083
B14 Ability to Identify Abnormal Psychological States	4.8667 ± 0.3519	0.8667	0.0723	0.0891	0.0083
B15 Prevention and Nursing Measures for Deep Vein Thrombosis	4.8667 ± 0.3519	0.8667	0.0723	0.0891	0.0083
B16 Nursing Measures and Identification of Pressure Injuries	4.8667 ± 0.5164	0.9333	0.1061	0.0891	0.0083
B17 Nutritional Support Nursing Observation and Identification/Management of Related Complications	4.8667 ± 0.3519	0.8667	0.0723	0.0891	0.0083
B18 Prevention and nursing management of accidental dislodgement of clinical tubes	4.9333 ± 0.2582	0.9333	0.0523	0.1415	0.0131
B19 Prevention and Nursing of Catheter-Associated Infections (PICC-related bloodstream infection, CVC-related bloodstream infection, ventilator-associated pneumonia, urinary catheter-associated UTI)	5.0000	1	0	0.1783	0.0166
B2 Neurosurgery Specialty Nursing Skills	5.0000	1	0	0.25	0.0929	0.0086
B21 Care of Neurosurgical Drainage Tubes (e.g., ventricular drain, hematoma cavity drain, lumbar drain)	5.0000	1	0	0.2797	0.0260
B22 Measures to Prevent and Manage Pulmonary Infections	4.8667 ± 0.3519	0.8667	0.0723	0.1246	0.0116
B23 Nursing Observation and Effect Evaluation During Sedative/Analgesic Medication Use	4.9333 ± 0.2582	0.9333	0.0523	0.222	0.0206
B24 Methods and Precautions for Physical Cooling	4.8667 ± 0.5164	0.9333	0.1061	0.1246	0.0116
B25 Early Rehabilitation Nursing Skills	4.8667 ± 0.3519	0.8667	0.0723	0.1246	0.0116
B26 Health Education Ability	4.8667 ± 0.3519	0.8667	0.0723	0.1246	0.0116
B3 Neurocritical Care	5.0000	1	0	0.25	0.0929	0.0138
B31 Intracranial Pressure Monitoring and Care	4.9333 ± 0.2582	0.9333	0.0523	0.1167	0.0108
B32 Management of Mechanical Assist Devices	4.8667 ± 0.3519	0.8667	0.0723	0.0756	0.0070
B33 Interpretation of Basic Ventilator Modes	4.8667 ± 0.3519	0.8667	0.0723	0.0756	0.0070
B34 Critical Care Treatment Nursing Measures	4.9333 ± 0.2582	0.9333	0.0523	0.1167	0.0108
B35 Emergency Response Capability for Seizures and Status Epilepticus	5.0000	1	0	0.1799	0.0167
B36 Emergency Rescue Capability for Cerebral Herniation	5.0000	1	0	0.1799	0.0167
B37 VTE Anticoagulation Therapy Monitoring and Severe Bleeding Emergency Management	5.0000	1	0	0.1799	0.0167
B38 Bedside Cooperation for Acute ICP Elevation Caused by Sudden Changes in Posture	4.8667 ± 0.3519	0.8667	0.0723	0.0756	0.0070
B4 Emergency Response Capability	5.0000	1	0	0.25	0.0929	< 0.001
B41 Application Capability of Emergency Equipment	5.0000	1	0	0.2	0.0186
B42 Emergency Response Capability for Cardiac/Respiratory Arrest	5.0000	1	0	0.2	0.0186
B43 Emergency Response Capability for Shock	5.0000	1	0	0.2	0.0186
B44 Cooperation and Timeliness in Endotracheal Intubation and Tracheostomy	5.0000	1	0	0.2	0.0186
B45 Emergency Response Capability for Massive Hemorrhage	5.0000	1	0	0.2	0.0186
C Communication and Coordination Ability	4.4667 ± 0.5164	0.4667	0.1156	0.1319	0.1319	< 0.001
C1 Effective Communication Skills	4.8667 ± 0.3519	0.8667	0.0723	0.6667	0.0879	< 0.001
C11 Effective Communication Ability with Patients and Families	5.0000	1	0	0.3333	0.0293
C12 Clear Written and Oral Communication Ability	5.0000	1	0	0.3333	0.0293
C13 Effective Communication with Other Medical Staff	5.0000	1	0	0.3333	0.0293
C2 Effective Coordination Ability	4.8 ± 0.414	0.8	0.0863	0.3333	0.0440	< 0.001
C21 Assisting Physicians in Emergency Care for Neurocritical Patients	4.6667 ± 0.7237	0.8	0.1551	0.5	0.0220
C22 Coordinating Medical Staff with Patients and Families	4.6 ± 0.7368	0.7333	0.1602	0.25	0.0110
C23 Fostering Cooperative Relationships Between Nursing Staff and Other Departments	4.6 ± 0.9103	0.8	0.1979	0.25	0.0110
D Research and Teaching Ability	4.0667 ± 0.5936	0.2	0.146	0.0624	0.0624	< 0.001
D1 Scientific Research Ability	4.2667 ± 0.7988	0.4667	0.1872	0.2	0.0125	< 0.001
D11 Ability to Collect Clinical Data and Analyze Data	4.8 ± 0.414	0.8	0.0863	0.25	0.0031
D12 Literature Retrieval, Thesis Topic Selection, and Writing Ability	4.8 ± 0.414	0.8	0.0863	0.25	0.0031
D13 Integrating Nursing Research into Clinical Practice	4.8667 ± 0.3519	0.8667	0.0723	0.5	0.0062
D2 Teaching Ability	4.5333 ± 0.9155	0.7333	0.2019	0.6	0.0374	< 0.001
D21 Clinical Teaching Ability	4.9333 ± 0.2582	0.9333	0.0523	0.5	0.0187
D22 Ability to Organize Teaching Rounds and Participate in Teaching Evaluation	4.9333 ± 0.2582	0.9333	0.0523	0.5	0.0187
D3 Learning Ability	4.2667 ± 0.5936	0.3333	0.1391	0.2	0.0125	0.0226
D31 Learning New Concepts and Knowledge	5.0000	1	0	0.3317	0.0041
D32 Grasping Discipline Status and Development Trends	4.8667 ± 0.3519	0.8667	0.0723	0.1972	0.0025
D33 Understanding Discipline Frontiers	4.8 ± 0.5606	0.8667	0.1168	0.1394	0.0017
D34 Regular Training Attendance	5.0000	1	0	0.3317	0.0041
E Professional Qualities and Ethics	4.0667 ± 0.4577	0.1333	0.1126	0.0624	0.0624	< 0.001
E1 Psychological Quality	4.3333 ± 0.488	0.3333	0.1126	0.25	0.0156	< 0.001
E11 Proactive Psychological Adjustment Ability	4.9333 ± 0.2582	0.9333	0.0523	0.3333	0.0052
E12 Strong Adaptability and Endurance	4.9333 ± 0.2582	0.9333	0.0523	0.3333	0.0052
E13 Optimistic, Cheerful, and Stable Emotional State	4.9333 ± 0.2582	0.9333	0.0523	0.3333	0.0052
E2 Professional Ethics and Conduct	4.6667 ± 0.488	0.6667	0.1046	0.75	0.0468	< 0.001
E21 Adherence to Professional Standards and Ethical Codes	5.0000	1	0	0.3333	0.0156
E22 Respecting Patients and Protecting Patient Rights (e.g., right to know, privacy)	5.0000	1	0	0.3333	0.0156
E23 Spirit of Prudent Autonomy (Maintaining integrity when unobserved)	5.0000	1	0	0.3333	0.0156

Discussion

This study established a rigorous process for developing neurosurgery specialist nurse competency indicators, involving systematic literature retrieval, screening, content analysis, and integration. A dedicated panel oversaw the entire framework construction to ensure methodological robustness. The preliminary indicators underwent refinement through two rounds of Delphi expert consultation, ultimately achieving moderate to good consensus with reliable outcomes. As a mature and widely recognized methodology for indicator screening, the Delphi method has been extensively applied across nursing discipline research [11].

The selection of 15 experts aligns with recommended sample sizes (15–50) for Delphi research, which is sufficient to ensure the reliability of consultation results [12]. Key findings confirmed high methodological quality: 100% valid response rate in both rounds, indicating expert engagement was exceptional; Authority coefficients (Cr) > 0.700, confirmed expert credibility; Mean scores > 3.5 points and coefficients of variation (CV) < 0.25 for all indicators, demonstrated strong expert coordination.

Given varying importance across indicators, the Analytic Hierarchy Process (AHP) was applied to determine relative and comprehensive weights based on round 2 data. The integration of the Delphi and AHP approaches could combine qualitative and quantitative strengths to establish scientifically defensible weightings. All consistency ratios < 0.1 for Level 1–3 indicators, validating rational weight allocation. Final composite weights were multiplicatively calculated to clarify hierarchical importance. According to the above methods, we believe the neurosurgery specialist nurse competency evaluation system constructed in the research was scientifically rigorous and reliable.

Compared with previous studies, most existing neurosurgical nursing competency frameworks only focus on professional skills or single clinical nursing dimensions [4], lacking systematic classification of competency modules; some studies have constructed hierarchical indicators but fail to assign scientific weights, which cannot reflect the priority of different competency requirements [6]. This study combines expert consensus with quantitative weight calculation, and the constructed system covers five comprehensive dimensions of professional knowledge, skills, communication, research and professional ethics, which is more in line with the actual clinical work needs of neurosurgical specialist nurses. In addition, this study adds the dimension of neurocritical care nursing indicators, which fills the gap of competency evaluation for neurosurgical ICU nurses, and has stronger clinical pertinence compared with previous general neurosurgical nursing competency tools.

However, this study also has some limitations. First, the constructed competency framework has not carried out clinical reliability and validity verification, and its actual clinical application effect needs to be further tested by empirical research. Second, all consulting experts are selected from tertiary hospitals in China, lacking experts from secondary hospitals, community medical institutions and foreign countries, so the research results have limited generalizability to other medical institutions and international medical systems. Third, although the initial indicator development process is systematic, the detailed extraction criteria of individual indicators need to be further improved to enhance the reproducibility of the study.

Conclusion

This study developed a scientific and comprehensive competency evaluation framework for neurosurgical specialist nurses through Delphi-AHP mixed method, which includes 5 first-level indicators, 14 s-level indicators and 64 third-level indicators. The framework has good expert authority, high coordination degree and scientific weight distribution, which can provide a standardized and practical tool for clinical training, performance evaluation and standardized management of neurosurgical specialist nurses.

Innovations

1. Combined Delphi and AHP methods to construct a weighted and hierarchical neurosurgical specialist nurse competency evaluation system, which makes up for the deficiency of single qualitative research in existing studies, and the indicator priority is clearer.

2. Integrated neurocritical care nursing-related competency indicators, focusing on the competency needs of neurosurgical ICU nurses, which is more consistent with the current clinical development of neurosurgical critical care medicine.

3. Carried out prospective research before the launch of China’s neurosurgical specialist nurse certification, and the developed framework can directly support the subsequent national specialist nurse training and certification work, filling the domestic gap in this field.

Limitations

1. The competency evaluation framework has not carried out clinical reliability and validity testing, and its actual application effect in nursing talent assessment and training needs to be further verified by clinical empirical research.

2. The research experts are only selected from Chinese tertiary hospitals, without including experts from different levels of medical institutions and different countries, resulting in limited generalizability of the research conclusions.

3. The detailed description of the initial indicator extraction and screening process is not comprehensive enough, and the reproducibility of the research needs to be further optimized.

Future directions

1. Carry out clinical validation research on the competency framework, test its reliability and validity, and optimize and adjust the indicators combined with clinical application feedback.

2. Expand the scope of expert selection, include experts from different levels of medical institutions and regions, and improve the generalizability of the framework.

3. Apply the established competency evaluation framework to the clinical training and assessment of neurosurgical specialist nurses, and form a complete set of nursing talent competency evaluation and training programs.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

S.L. determined the research direction and led the development of the competency indicators, contacted and consulted experts during the Delphi process, and was responsible for research funding application and management. W.L. organized the Delphi consultation materials and performed the statistical analysis.Z.W. conducted the literature search and data collection. All authors read and approved the final manuscript.

Funding

This study was supported by Wangjing Hospital, China Academy of Chinese Medical Sciences (Grant No. WJYY-ZZXT-2023-09). The funding body had no role in the study design, data collection, analysis, interpretation, or manuscript preparation.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality agreements with the Delphi experts, but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

All experts participating in this study provided written informed consent prior to the Delphi consultation. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Wangjing Hospital, China Academy of Chinese Medical Sciences.

Consent for publication

Not applicable.

Authors’ information

S.L. is Deputy Chief Nurse at Wangjing Hospital, China Academy of Chinese Medical Sciences, with research interests in neurosurgical nursing education and management. W.L. is a Senior Nurse at Wangjing Hospital, China Academy of Chinese Medical Sciences, specialising in neurosurgical nursing.

Competing interests

The authors declare no competing interests.