Building a Public Health Nursing Competency Framework

According to the Development of a competency framework for public health nurses in China: A modified Delphi study, published in October 2026, researchers developed a context-specific framework containing six domains, 16 subdomains, and 46 competencies. The study combined a literature review, policy-based job analysis, semi-structured interviews with 22 practitioners, and a two-round Delphi consultation with 21 experts.

• According to the study published on October 30, 2026, health education and health promotion received the highest domain weight, 0.280, in the final framework.
• According to the researchers, the preliminary framework contained 52 competencies and the final framework contained 46 competencies after expert review and refinement.
• According to the study, Delphi response rates were 95.45% in Round 1 and 100% in Round 2, while the expert authority coefficient was 0.90.
• According to the interview findings, 22 practitioners from seven primary healthcare settings in two Sichuan cities contributed frontline perspectives.
• The study authors wrote that the framework provides a structured reference for public health nurse education, in-service training, workforce development, and professionalization in China.

The researchers built the public health nursing competency framework in two phases, according to the methods section of the October 2026 PLOS One study. Phase 1 combined a literature review, analysis of national public health service standards, and semi-structured interviews with frontline practitioners. Phase 2 used two rounds of structured expert consultation to refine the framework.

The literature review identified 9, 547 records, removed 3, 628 duplicates, excluded 5, 833 records during title and abstract screening, and excluded 46 records after full-text review, according to the study results published in October 2026. The researchers included 40 publications in the framework development process.

The qualitative interview component included six physicians, 11 nurses, and five administrators, according to the study. Researchers used maximum-variation purposive sampling across urban, urban-rural fringe, and township facilities, and they reported a mean interview duration of 34 minutes.

The study analyzed interview data through thematic analysis in NVivo 11, with two researchers independently coding transcripts and resolving discrepancies through discussion. The authors stated that the interviews explored service responsibilities, current competency gaps, required competencies, and strategies for strengthening public health nursing.

The study defined public health nursing in China according to local practice rather than a separate official professional title. The authors explained that public health nurses are nurses in primary healthcare institutions who perform responsibilities including disease prevention, health promotion, health education, chronic disease management, and health management for key populations.

Healthcare researchers can use the study’s multi-source design as a model for building competency frameworks that connect lived experience, policy requirements, and expert judgment. According to the October 2026 study, the strongest framework was not produced by Delphi ratings alone; the researchers first triangulated literature, job standards, and practitioner interviews.

Qualitative researchers should separate consensus from prioritization when interpreting Delphi findings. The study reported Kendall’s coefficients of concordance of 0.156 in Round 1 and 0.171 in Round 2, both statistically significant at P < 0.001, while the authors noted that the relatively low values suggested limited agreement on competency prioritization despite item-level consensus.

Nursing educators can use the six-domain structure to map curricula and professional development activities to defined competencies. The study assigned health education and health promotion a weight of 0.280, community health surveillance and assessment a weight of 0.196, and health management a weight of 0.196.

Research teams can use AI-enabled qualitative analysis to reduce manual effort without delegating methodological decisions to an algorithm. AI can cluster similar interview excerpts, compare expert comments between Delphi rounds, identify additions and removals, and surface contradictions for human review. Researchers must still document sampling, coding decisions, consent restrictions, and expert validation.

The study authors cautioned that the framework requires further empirical validation, broader regional and multidisciplinary samples, pilot implementation, psychometric evaluation, and measurable learning outcomes. AI-generated themes should therefore be treated as analytical support rather than proof that a competency is valid or transferable.

The October 2026 PLOS One study demonstrates how literature, policy analysis, frontline interviews, and Delphi consultation can produce a practical public health nursing competency framework. The study’s 22 interviews, 21-expert Delphi panel, six domains, 16 subdomains, and 46 final competencies provide a clear example of multi-source qualitative synthesis.

AI-enabled qualitative research can make this workflow more efficient by helping teams search, code, compare, and interrogate large collections of interviews and documents. Human researchers must retain responsibility for interpretation, ethics, consensus decisions, and validation.

Researchers analyzing competency frameworks, healthcare interviews, or expert consultations can Try Evidano for free to accelerate evidence synthesis while keeping the analytical process reviewable.