Risky Indebtedness Behavior: Impacts on Financial Preparation for Retirement and Perceived Financial Well-Being
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".