The research team, led by Colleen P. Nofi, Ila Sethi, and Vihas Patel of Northwell Health and the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, together with collaborators at Good Samaritan Regional Medical Center and Hackensack University Medical Center, designed an exploratory prospective observational cohort study spanning the 2022–2023 academic year. Their central question was deceptively simple: does a resident’s emotional intelligence, commonly abbreviated EI, predict the degree of agreement between how residents rate themselves and how their programs’ Clinical Competency Committees, the faculty panels formally charged with assessing trainee progress, rate them?

To measure emotional intelligence, the investigators used the 33-item Schutte Self-Report Emotional Intelligence Test, a validated instrument rooted in the Salovey and Mayer model of EI as the ability to monitor, discriminate among, and use emotional information. Self-assessment alignment was quantified using the Accreditation Council for Graduate Medical Education’s Milestones 2.0 framework, a competency-based evaluation system that grades residents across specialty-specific subcompetencies. Concordance between resident self-evaluations and Clinical Competency Committee evaluations was captured at two time points, mid-year and end-of-year, allowing the team to track not just whether residents were accurate, but whether their insight improved as the training year unfolded.

The dataset included performance evaluation data from 88 surgical residents whose concordance could be analyzed; of those, 36 also completed the emotional intelligence survey. The headline result was a trend rather than a definitive statistical verdict: residents with medium to high EI scores demonstrated greater concordance with their Clinical Competency Committee evaluations than residents with low EI scores, with a p-value of 0.059, just shy of the conventional threshold for statistical significance. In an exploratory study of this size, the researchers argue, such a near-significant association is meaningful evidence that self-awareness, a core component of emotional intelligence, underpins honest and accurate self-appraisal of surgical competence.

The temporal data added a second, arguably more hopeful, layer to the story. At mid-year, 43 percent of residents showed low concordance between self-assessment and faculty evaluation, while 57 percent showed high concordance. By the end of the academic year, low concordance had fallen to 38 percent and high concordance had climbed to 62 percent. In other words, residents as a group became more accurate judges of their own performance over time, a pattern the authors attribute plausibly to the accumulating effect of ongoing feedback, formal evaluations, and the reflective practice embedded in surgical training.

Perhaps the most unexpected finding concerned program size, a variable rarely scrutinized in competency research. Residents in medium-sized programs achieved strikingly higher concordance, at 86 percent, than those in small programs, at 58 percent, or large programs, at 52 percent, a difference that reached statistical significance with a p-value of 0.029. The authors offer no single explanation, but the pattern invites speculation about the social dynamics of feedback: in mid-sized programs, faculty may know each resident well enough to give calibrated, individualized evaluations, while the smallest programs may suffer from limited evaluator diversity and the largest from anonymity, with residents and faculty interacting too briefly for honest, granular assessment. Notably, neither postgraduate year level nor program type showed any significant association with concordance.

The significance of accurate self-assessment extends well beyond academic bookkeeping. Prior work has repeatedly documented that residents frequently overestimate their abilities relative to faculty judgment, and the milestone framework under which these evaluations occur directly shapes promotion, remediation, and ultimately board certification. A resident who cannot accurately perceive a competency gap may fail to seek the operative experience or study needed to close it, while a program that cannot rely on self-assessment must invest heavily in external surveillance. The new study suggests that emotional intelligence could serve as a screening variable, identifying trainees who need structured support in developing the self-awareness that honest self-evaluation demands.

The findings also sit within a growing body of literature linking emotional intelligence to surgical outcomes. Earlier studies have associated EI with resident well-being, lower burnout, higher job satisfaction, and even technical performance measures such as surgical quality. A 2020 pilot study by Nayar and colleagues reported that emotional intelligence predicted accurate self-assessment of surgical quality, and the present work extends that logic from the individual procedure to the full spectrum of clinical competencies codified by the ACGME. Educational researchers have begun designing interventions, from patient-centered experiences to formal EI coaching curricula, aimed at cultivating these skills deliberately rather than assuming they emerge from clinical exposure alone.

The authors are careful to frame their conclusions within the study’s limits. With only 36 residents completing the EI survey, the cohort was small and the near-significant p-value could shift in either direction with more data; the self-report nature of the Schutte test introduces the possibility that residents with genuinely high self-awareness also rate their EI differently; and the multi-institutional design, while a strength for generalizability, leaves residual confounding from institutional culture and evaluation practices unmeasured. Data privacy constraints prevent open sharing of the underlying dataset. Still, the study’s exploratory design was explicitly intended to generate hypotheses, and its results justify larger, powered trials of EI measurement and training in surgical education.

If confirmed, the implications for training programs are concrete. Programs might incorporate validated EI assessments at entry, use concordance between self- and faculty evaluations as a flag for residents lacking insight, and design feedback structures, particularly in small and large programs, that replicate the calibrated, personal evaluation environment apparently achieved in mid-sized ones. The steady improvement in self-assessment accuracy across the academic year reinforces a message educators have long promoted but rarely quantified: feedback, delivered consistently and received with genuine self-awareness, teaches residents not just how to operate, but how to see themselves as surgeons. In a profession where the cost of misjudging one’s own limits is measured in patient outcomes, cultivating that inner clarity may be one of the most consequential skills a training program can teach.

Subject of Research: The relationship between emotional intelligence and the accuracy of surgical residents' self-assessed clinical competency

Article Title: Emotional intelligence influences surgical resident’s self-perception of competency: an exploratory, multi-institutional study

Article References: Nofi, C. P., Sethi, I., Demyan, L., Koti, S., Hansen, L., Serfin, J., Surick, B., & Patel, V. (2026). Emotional intelligence influences surgical resident’s self-perception of competency: an exploratory, multi-institutional study. Global Surgical Education – Journal of the Association for Surgical Education, 5(1), Article 166. https://doi.org/10.1007/s44186-026-00568-6

Image Credits: AI Generated

DOI: 10.1007/s44186-026-00568-6

Keywords: emotional intelligence, surgical residents, self-assessment, Clinical Competency Committee, ACGME Milestones 2.0, surgical education, competency evaluation, Schutte Self-Report Emotional Intelligence Test, graduate medical education, feedback, self-awareness, Emotional

The scientific foundation of DQPA rests on two pillars of international standardization. ISO 8000 establishes the principles of data quality management, while the ISO/IEC 33000 family provides the general mechanism for process capability assessment: a process reference model, process attributes rated on an ordinal scale, and a maturity model that aggregates those attributes into organizational levels. Although this mechanism has a long track record in domains such as software development, Green IT, and data quality certification, no international instantiation had ever covered data governance, data management, and data quality management jointly as integrated disciplines. The only ISO instantiation for the data domain, the ISO 8000-6x series, is confined to data quality management alone. The researchers built on a direct precedent, the MAMD model, and on the UNE 0077 through 0080 specifications—what they describe as the first standardization initiative to close that gap with normative status—enriched with the governance principles of ISO/IEC 38505 and the DAMA-DMBOK body of knowledge.

What distinguishes self-assessment from formal certification is its purpose. Under ISO/IEC 33000, assessment by an independent team enables certification with validity toward third parties, while self-assessment, performed by the organization itself, is an equally recognized application of the same method aimed at understanding one’s own situation recurrently and affordably as a basis for continuous improvement. Until now, no adequate instrument has existed for this second application, for two reasons. Independent assessment is too costly to repeat frequently, and its most expensive phase—evidence collection—depends heavily on tacit human knowledge that is difficult to automate. Moreover, translating normative processes into language that business profiles can act upon, one of the very purposes of data governance, rarely occurs in manual practice. Existing frameworks fall short in different ways: COBIT 2019 lacks an integrated data-domain maturity model; DCAM and CMMI-DMM support self-assessment but are not grounded in ISO/IEC 33000; and DAMA-DMBOK systematizes the disciplines without defining a maturity model of its own. None combines integrated coverage, an ISO/IEC 33000-based mechanism, automated scoring, automated recommendations, and open-source availability.

DQPA’s architecture is deliberately engineered around a strict separation between deterministic computation and generative artificial intelligence. The platform is a multilayer web application: a React single-page client, a Node.js and Express server exposing a REST API and hosting the deterministic assessment engine, a MongoDB document store accessed through Mongoose, and an external large language model service invoked only after results have been computed. Authentication is token-based with role-based access control, and both tiers deploy as independent containers. Crucially, the normative model itself—processes, questions, weightings, and improvement tasks—is maintained as configurable data through an administration module restricted to expert users, meaning the platform can adapt to revisions of the specifications or even to equivalent frameworks without touching the source code. This data-driven design is what makes the tool reusable and future-proof in a way that hard-coded assessment instruments cannot be.

The heart of the platform is its assessment engine, which algorithmically reproduces the measurement framework of ISO/IEC 33020. Question answers are first converted into weighted percentage scores at two granularities: one per process, from process-specific capability-level-1 questions, and one per capability level from 2 to 5, from cross-cutting questions shared across the scope. The four-category achievement scale maps onto these scores: Not implemented for 0 to 15 percent, Partially implemented for above 15 to 50 percent, Largely implemented for above 50 to 85 percent, and Fully implemented for above 85 to 100 percent. The staged aggregation rule then applies: a process reaches a given capability level when its process-specific score and all lower cross-cutting scores are Fully achieved and the score at that level is at least Largely achieved. The organizational maturity level, on a six-level scale from 0 to 5, is derived from the consolidated capability of the assessed processes in a staged manner. The entire computation is deterministic, traceable, and executed without any intervention from humans or the AI service—a design choice the authors argue is a property rather than a limitation, because transparency and auditability are explicit requirements of the ISO/IEC 33000 method itself.

Only after the numbers are settled does artificial intelligence enter the picture—and the boundaries are strict. The recommendation service neither trains nor fine-tunes any model. Instead, a pre-trained language model, Gemini 2.0 Flash Lite, selected after a structured comparison against alternatives including GPT-4o and GPT-4.1 nano for its large context window and high throughput, is conditioned at inference time by a purpose-built structured prompt. The model’s grounding is entirely deterministic: its input consists solely of the computed as-is state—levels, ratings, and gaps—and a catalogue of predefined improvement tasks curated by domain experts from an anonymized corpus of real projects, assessment reports, standards, and technical documentation. The prompt explicitly forbids the re-computation of levels and imposes prioritization criteria including impact on maturity, criticality of the gap, dependencies, feasibility, urgency, and normative alignment. If the model call fails, the service degrades gracefully to a template-based ordering of the curated catalogue, so a usable plan is always produced without the generative component.

The platform was validated in striking fashion against a real organization: a Spanish public river basin management body responsible for hydrological data acquisition and exploitation. Questionnaire responses were recorded in parallel through DQPA and through the manual procedure of an external expert assessment team, with each business process owner completing the instrument in roughly two and a half hours with support from assessors. The engine computed a largely achieved process-specific score, a partially achieved level-2 dimension, and an unattained level-3 dimension, yielding maturity level 1—precisely the level determined independently by the human experts. Verification went further: the engine’s logic was exhaustively checked against a reference spreadsheet used by the consulting team in professional practice, across all 1,024 possible rating combinations, with full agreement in every case, including boundary conditions between capability levels.

The quality of the AI-generated recommendations was then scrutinized by four expert evaluators—three of them external to the author team and blind to the study—who rated thirty recommendations on a five-point rubric covering consistency with the computed state, alignment with the specifications, actionability, and clarity for non-technical profiles. The overall mean was 4.35 out of 5, with no two evaluators differing by more than one point on any of the 480 ratings, and the restricted external-only mean of 4.23 confirmed the result does not depend on the internal rater. The platform was also applied to three further organizations—a local public administration, a port authority operating critical infrastructure, and a large private technology corporation—producing consistent operation across markedly different sectors, with resulting maturity levels ranging from 0 to 1. Perceived usability, measured with the System Usability Scale across four participants, averaged 80.6, comfortably above the scale’s commonly cited average of about 68. Performance testing showed the deterministic engine computing results in a median of 105 milliseconds and sustaining 150 concurrent users, while end-to-end report generation took a median of three seconds, dominated by the external model call.

The implications reach well beyond convenience. For public administrations and resource-constrained organizations facing obligations under the European Data Governance Regulation, DQPA substantially lowers the barrier to understanding and improving their data practices without depending on scarce certified assessors. For researchers, the platform’s elimination of inter-assessor variability in the computation phase opens the door to genuinely reproducible, longitudinal, and sector-level empirical study of data maturity—questions such as which processes systematically act as bottlenecks, or how maturity evolves after improvement plans are applied, that have been nearly impossible to address empirically until now. The authors are careful to delimit their claims: the platform does not verify declared evidence, cannot prevent deliberate misstatement, does not replace third-party certification, and its recommendations must be contextualized by each organization since the AI knows nothing of internal budgets or politics. Future work includes ablation studies contrasting catalogue-anchored with unconstrained generation, a self-hosted AI deployment for stricter data-residency control, sector-level benchmarking, and what-if simulation of improvement scenarios under explicit resource constraints. But the core message is already clear: the machinery once reserved for expensive consulting engagements has been reproduced as transparent, inspectable, open-source software that any organization can pick up and run.

Subject of Research: An open-source software platform for ISO/IEC 33000-based self-assessment of data governance, data management, and data quality maturity

Article Title: DQPA: A software platform for ISO/IEC 33000-based self-assessment of data governance, data management, and data quality maturity

Article References: Gualo, F., Ayuso, Y., Caballero, I., & Piattini, M. (2026). DQPA: A software platform for ISO/IEC 33000-based self-assessment of data governance, data management, and data quality maturity. SoftwareX, 35, Article 103012. https://doi.org/10.1016/j.softx.2026.103012

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103012

Keywords: data governance, data quality, data management, maturity models, ISO/IEC 33000, ISO 8000, self-assessment, process capability, large language models, open-source software, SoftwareX, UNE 0080