The wellbeing economy, as the authors frame it, is an economy that prioritizes people and planet over profit. The Organization for Economic Cooperation and Development defines the Economy of Wellbeing as the capacity to create a virtuous circle in which citizens’ wellbeing drives economic prosperity, stability and resilience, and vice versa, with good macroeconomic outcomes sustaining wellbeing investments over time. According to the review, the concept is gaining support among governments, policymakers, budget holders, businesses and civil society, and it has been adopted in various forms by countries including Scotland, Wales, New Zealand, Finland, Iceland, Costa Rica and Bhutan. Wales, for instance, has enacted the Wellbeing of Future Generations Act 2015 and the Social Partnership and Public Procurement Act 2023, while New Zealand introduced a Wellbeing Budget in 2019 focused on child wellbeing and economic transformation.

The technical backbone of the new framework is a set of four cross-cutting policy priorities put forward by the World Health Organization, known as wellbeing capitals, which integrate human, social, planetary and economic wellbeing. Healthy societies, the authors note, build economies that ensure the prosperity of people, civil society and the planet by maintaining universal policies for housing and food, lifelong learning and literacy, and a stable ecological balance, with equity, inclusion and sustainability as guiding principles. Although many countries have recognized these four capitals as critical and direction-setting, the review points out that they had not been integrated into wellbeing economy research so far. That gap matters because WHO/EURO has identified twelve stakeholder groups that should undertake four key actions to promote, implement and evaluate the wellbeing economy approach, and the public health workforce operates across many of them, from academia and ministries to banks and funding bodies.

Methodologically, the team followed the Joanna Briggs Institute guidance for scoping reviews and the PRISMA Extension for Scoping Reviews, registering a protocol on the Open Science Framework. The review used the JBI mnemonic PCC, with population attributed to public health reflecting the health of people and the planet, the concept of interest being the wellbeing economy and its capitals, and the context comprising competency-based education and training frameworks. The researchers searched four databases, Medline via PubMed, EMBASE via Ovid, Global Health via EBSCOHost and ERIC via EBSCOHost, with the final database search conducted on January 15, 2025. They then supplemented this with targeted searches of multilateral organizations, including the WHO European Wellbeing Economy Initiative, the WHO Institutional Repository for Information Sharing, the Wellbeing Economy Alliance, the OECD and the World Bank, followed by backward and forward handsearching and direct contact with corresponding authors.

The scale of the screening effort was considerable. A total of 3,965 titles were identified through the four mechanisms, and after deduplication in Rayyan, pilot testing and independent full-text review, 27 documents representing 23 units of analysis were included. From these, the team extracted 38 frameworks, 54 domains and 423 competencies. The synthesis relied on a cross-walking process methodology previously used to study competencies in public health professional education: competencies were extracted and confirmed by two authors, verbs and descriptions were analyzed to eliminate duplicates and identify emerging competencies, and each competency was then mapped against one or all four actions of the wellbeing economy. Where the mapping revealed knowledge gaps that could not be filled from the literature, the researchers proposed new competencies themselves.

The result is a proposed Competency Framework with five domains and 31 competencies, 30 derived from the review and one proposed by the researchers. Domain one, Ethics and professionalism, contains five competencies covering self-awareness of one’s own behavior and values regarding humanity, acting openly and transparently according to the rule of law, understanding cultural differences by examining unconscious bias, challenging power differentials, and making evidence-informed decisions. Domain two, Determinants of health and wellbeing, is the largest with eight competencies, spanning action on wider determinants including climate and environmental change, protection of the environment, housing security, access to healthy foods and reduction of food insecurity, employment security and workplace fairness, school enrollment and literacy, access to quality primary care, and assessment of the long-term impacts of the economy on neighborhood-level advantage.

Domain three, Digital transformation and data literacy, reflects the increasingly technical character of modern public health practice. Its five competencies include distinguishing and using diverse data sources to interpret and evaluate health information and digital content, sorting facts from misinformation and disinformation, addressing gaps in digital literacy and integrating them into education and training programs, engaging and empowering citizens and communities to participate in the wellbeing economy through digital media, and promoting public understanding and awareness of artificial intelligence technologies. The inclusion of AI literacy is notable, and the source material the team drew on included OECD work on digital government skills, the DigComp 2.2 framework for citizens’ digital competence, and OECD analyses of the impact of digital technologies on wellbeing.

Domain four, Policy, politics and governance, contains six competencies focused on working across systems and sectors. These include contributing to systems that strengthen collaboration, communication and coordination across sectors and stakeholders for societal wellbeing, engaging with multiple governing sectors from local to international levels, making informed judgments about complex wellbeing and health policy issues, securing sustainable funding for health and wellbeing programs, engaging in policy development and implementation, and developing adaptive global health security frameworks with continuous monitoring, evaluation and improvement. Domain five, Building capacity for sustainable wellbeing economy, holds seven competencies emphasizing lifelong learning, leadership and entrepreneurship as agents of change, systems thinking and critical thinking to understand key drivers of wellbeing outcomes, economic evaluation methods to capture wider social benefits of health programs, expansion of provision and funding of care, social care, education and transportation services, improvement of regulation and funding of macroeconomic systems for wellbeing goals, and the use of tools to steer economic investment toward wellbeing goals.

The authors argue that the framework fills a genuine void. Existing competency frameworks for the public health workforce, such as the WHO-ASPHER Competency Framework for the Public Health Workforce in the European Region and the UK Faculty of Public Health Core Competences, were developed before the emergence of the wellbeing economy and its associated capitals, and primarily focus on public health functions. While both contain competencies related to wellbeing, such as building strategic alliances to improve population health or using public health intelligence to assess wellbeing, neither includes domains specifically dedicated to the wellbeing economy. The new framework is explicitly centered on the four capitals and maps competencies against the four actions of the wellbeing economy agenda, including fair and equitable investment in the capitals, delivery of public health goods and policies for healthy lives, and collaborative work across government and society. The authors also connect the ethics domain to the Inner Development Goals initiative, which promotes inner growth as a prerequisite for sustainable outer change.

The timing is significant. The final report of the WHO Council on the Economics of Health for All highlighted the urgent need for synergetic, coordinated work across disciplines, sectors and levels of government, leading WHO and its Member States to adopt a Resolution on the Economics of Health for All in 2024. A WHO Global Strategy is being developed for adoption in 2026, and WHO/EURO is progressing the agenda through a Wellbeing Economy Initiative focusing on public health priorities for investment with multiple co-benefits, including ageing well, youth mental health and inclusion, and addressing rural-urban disparities. The review’s limitations are acknowledged: only English-language literature was included, and the framework does not yet prioritize which competencies matter most for each stakeholder group, so the authors recommend complementing it with a consensus study such as expert panels or a Delphi technique. Still, they conclude that the framework is an important step toward defining the field, providing a basis for training, performance assessment, training needs assessment and public health policy development, and serving as an actionable tool showing how public health stakeholders and leaders can best improve the wellbeing of their communities and citizens while building more resilient, sustainable and prosperous economies for all.

Subject of Research: wellbeing economy competency framework in public health

Article Title: Towards a competency framework for wellbeing economy in public health: a scoping review

Article References: Rodríguez-Feria, P., Czabanowska, K., Dyakova, M., Giraldo-Noack, N., & Clemens, T. (2026). Towards a competency framework for wellbeing economy in public health: a scoping review. Public Health Reviews, 47, Article 1609947. https://doi.org/10.3389/phrs.2026.1609947

Image Credits: AI Generated

DOI: 10.3389/phrs.2026.1609947

Keywords: wellbeing economy, public health, competency framework, scoping review, WHO, OECD, health policy, Towards, competency, framework, wellbeing, economy

The study, published in BMC Nursing, was led by Xiaobei Ma and Xueyan Fan, who contributed equally, together with colleagues including Fengli Gao, Xin Liu, Yi Duan, and corresponding author Zhifeng Gao. Rather than relying on a generic nursing checklist, the team constructed a framework tailored specifically to the role of the nurse anesthetist, a profession in which errors in airway management, drug dosing, or vigilance during the post-anesthesia care phase can have immediate and serious consequences. The result is a hierarchical instrument comprising five domains, twenty second-level indicators, and one hundred individual scored elements, each anchored to explicit behavioral criteria.

Building such an instrument required a multi-stage methodology that blends structured expert consensus with quantitative weighting. The researchers began with a literature review and semi-structured interviews with experts, then ran a two-round Delphi consultation, a technique in which a panel of specialists independently rates proposed items and receives feedback between rounds until agreement stabilizes. To determine how much weight each domain should carry in the final score, they applied the analytic hierarchy process, a mathematical method that converts pairwise expert judgments into a consistent set of weights, checking that the judgments did not contradict one another through a consistency ratio threshold.

The preliminary measurement evaluation focused on content validity and reliability. Experts rated the relevance of each item, yielding a scale-level content validity index average of 0.95, very close to the perfect score of 1.0, while item-level modified kappa values, which correct expert agreement for chance, ranged from 0.831 to 1.000. To estimate inter-rater reliability, five nurses were independently scored by two prespecified evaluators at program entry; the total-score intraclass correlation coefficient using the two-way mixed-effects absolute-agreement single-measure definition, ICC(A,1), was 0.889, with a 95 percent confidence interval of 0.388 to 0.988. The authors are candid that the narrow confidence interval’s lower bound reflects the tiny reliability subsample, making this preliminary, low-precision evidence rather than comprehensive validation.

The longitudinal component is where the study becomes genuinely distinctive. The team retrospectively analyzed a census of all eligible nurses entering the training program between August 2016 and August 2025, defining T0 as program entry after pre-employment preparation and T1 through T6 as the completion of each of the first six months. Of 77 screened records, 68, or 88.3 percent, had complete T0 through T6 data and entered the analysis; five nurses resigned during the period and four had at least two consecutive missing monthly assessments. All analyses were performed in R version 4.5.2, with repeated-measures analysis of variance serving as the primary within-evaluator test of change over time.

The trajectory of improvement was striking. Mean self-assessment scores climbed from 35.95 at entry to 96.83 at month six, while faculty ratings rose from 29.20 to 95.32. After applying the Greenhouse-Geisser correction, which guards against violations of the statistical assumption of sphericity in repeated-measures designs, the time effects were highly significant for both perspectives, with P values below 0.001 and partial eta-squared values of 0.977 for self-ratings and 0.984 for faculty ratings, meaning that time explained nearly all of the variance in scores. In practical terms, new nurse anesthetists moved from roughly a third of the maximum score to near-ceiling performance within six months under this framework.

Perhaps the most psychologically interesting finding concerns the gap between self-perception and external judgment. At every single time point, self-ratings exceeded faculty ratings, but the size of that gap changed systematically. A participant-level mixed-effects model, which accounts for repeated observations nested within individuals, estimated the model-based difference between evaluator trajectories as largest at month two, at 9.51 points, and shrinking to just 1.50 points by month six. The time-by-evaluator interaction was significant in a likelihood ratio test yielding a chi-square statistic of 110.67 with six degrees of freedom and P below 0.001. The pattern suggests that new nurses begin with a degree of overconfidence relative to their supervisors’ assessments, then calibrate their self-appraisal as real clinical experience accumulates.

The framework also showed meaningful concurrent associations with established assessment tools. At month three, self-ratings correlated with written examination scores at a Spearman coefficient of 0.815 and faculty ratings at 0.582, while the corresponding correlations with objective structured clinical examination scores, the standardized practical tests widely used in medical education, were 0.580 and 0.558. By month six these associations remained positive, at 0.752 and 0.532 for the written examination and 0.689 and 0.340 for the OSCE. Notably, at program entry the correlations with the written examination were negligible, which makes sense: before any anesthesia-specific training, general knowledge tests say little about how a nurse will perform in the operating room, so the framework’s scores capture something the written test does not.

The authors also report an exploratory analysis in a separate prespecified five-person subset examining whether every individual improved from T0 to T6. All five showed positive change, but an exact two-sided sign test produced a P value of 0.0625, just shy of the conventional 0.05 threshold, a mathematical inevitability when only five participants are tested, since the smallest achievable two-sided P value for a sign test with five observations is exactly 0.0625. The team treats this honestly as suggestive rather than confirmatory, and the supplementary materials go further, documenting acceptability questionnaires completed by all 68 respondents, sensitivity analyses, and exploratory safety surveillance, an unusually transparent level of statistical disclosure for a single-centre education study.

What the study does not claim is perhaps as important as what it does. The authors explicitly state that their results provide preliminary, low-precision measurement evidence rather than comprehensive validation or causal proof that the training program itself drove the score increases, and they call for prospective multicenter evaluation before the framework is used for any high-stakes decisions such as hiring, promotion, or certification. Still, the implications are considerable. With anesthesia teams worldwide facing persistent staffing pressures, a validated, behaviorally anchored, month-by-month competency map could help educators target support precisely where new nurses lag, and the finding that self-assessment converges with faculty judgment over time offers a measurable signal of professional maturation. If larger multicenter studies replicate these trajectories, the humble scoring rubric developed in a Beijing hospital could become a template for how surgical specialties everywhere turn the anxious first months of a new clinician’s career into a quantified, coachable journey.

Subject of Research: Development and longitudinal evaluation of a competency assessment framework for newly recruited nurse anesthetists

Article Title: Development, preliminary measurement evaluation, and longitudinal application of a competency assessment framework for newly recruited nurse anesthetists

Article References: Ma, X., Fan, X., Gao, F., Liu, X., Duan, Y., & Gao, Z. (2026). Development, preliminary measurement evaluation, and longitudinal application of a competency assessment framework for newly recruited nurse anesthetists. BMC Nursing. https://doi.org/10.1186/s12912-026-05424-y

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05424-y

Keywords: nurse anesthetists, competency framework, Delphi method, longitudinal assessment, mixed-effects model, content validity, inter-rater reliability, OSCE, nursing education, self-assessment, analytic hierarchy process, BMC Nursing