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Record W4389917076 · doi:10.3390/jrfm16120519

Risky Indebtedness Behavior: Impacts on Financial Preparation for Retirement and Perceived Financial Well-Being

2023· article· en· W4389917076 on OpenAlexvenueno aff
Kelmara Mendes Vieira, Taiane Keila Matheis, Ana Maria Heinrichs Maciel

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFinanceFinancial planStructural equation modelingFinancial analysisBusinessEconomics

Abstract

fetched live from OpenAlex

This study aimed to verify the impact of financial preparation for retirement and risky indebtedness behavior on perceived financial well-being. A survey was carried out with 2290 individuals from diverse sociodemographic and economic profiles who resided in Brazil. Confirmatory factor analysis and structural equation modeling were used as data analysis techniques. The results obtained indicate that risky indebtedness behavior negatively impacts financial preparation for retirement and perceived financial well-being and that there is a positive impact of financial preparation for retirement on perceived financial well-being. These findings highlight the importance of financial planning and savings behavior so that future expectations are achieved, and individuals may enjoy life with financial well-being. Thus, it is essential that public policies that promote new behaviors and healthy financial habits to the population, in addition to incentives for financial preparation for retirement, are built. Brazil needs to review the new credit concessions so that the individual does not acquire the behavior of using a financial resource that they do not have and that compromise financial well-being in the short and long term, negatively affecting retirement.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.250
Teacher spread0.239 · 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 teacher head, not a consensus.

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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