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Record W4409607302 · doi:10.3390/jrfm18040219

Financial Literacy and Leadership Skills Among Healthcare Professionals in Greece

2025· article· en· W4409607302 on OpenAlexvenueno aff
Georgios Pakos, Panagiotis Mpogiatzidis

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyHealth professionalsHealth careBusinessMedical educationPsychologyFinanceNursingMedicineEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Healthcare professionals require comparable knowledge and abilities in hospital financial administration. In addition, many physicians are not equipped to manage the leadership roles that healthcare systems require, namely the capacity to express a vision, convey it to others, garner willing support for it, and enable others to be leaders in return. Previous studies have demonstrated that physicians often lack financial literacy, while a recent systematic review and meta-analysis showed that healthcare professionals lack adequate financial literacy, although top healthcare practitioners and executive nurse leaders are encouraged to develop knowledge and abilities outside of their clinical specialty. The need for medical practitioners to receive training and experience in medical leadership has also been discussed in earlier studies. In Greece, evidence regarding the financial literacy levels and leadership skills among healthcare professionals is lacking, although physicians and nurses are required to obtain managerial and administrative roles as they progress in their positions. Our objective was to assess healthcare professionals’ levels of financial literacy and investigate the relationship between financial literacy and leadership skills in Greece. We conducted a prospective, multi-centered, question-based survey among healthcare professionals in several institutions in Northern Greece. Participants were asked to fill out basic demographic questions, the OECD/INFE Toolkit for Measuring Financial Literacy and Financial Inclusion 2022, and the Leadership Skills questionnaire, translated into Greek. The factorability of the questionnaires was examined with factor analysis, while the internal consistency was examined with Cronbach’s alpha. A linear correlation of leadership scores with financial literacy scores was performed with the Spearman rho, and multivariate regression analysis examined the correlation of the leadership score with financial literacy scores, adjusted for the type of task, education, status, gender, and age. The overall financial literacy score for all healthcare professionals was 69.14 ± 13.25%, which was higher compared to the average for the Greek population. Male healthcare professionals with administrative tasks had significantly higher overall financial literacy and digital financial literacy scores than females, or professionals without administrative tasks, as well as higher scores in all areas of leadership. Physicians had significantly higher overall financial literacy scores than nurses and significantly lower digital financial behavior and digital finance trend scores. Still, physicians scored significantly lower than nurses in all areas of leadership skills. There was a strong correlation between overall financial and digital financial literacy scores with leadership skills scores. Future research is warranted to explore how formal financial and leadership education included in the training programs of healthcare professionals would empower physicians by enabling them to make proactive decisions regarding their financial and managerial destiny.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.252
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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