A Systematic Literature Review on Personal Financial Well-Being: The Link to Key Sustainable Development Goals 2030
Bibliographic record
Abstract
This study presents systematic literature review (SLR) of financial well-being which is crucial for attaining several key UN Sustainable Development Goals 2030 (SDG 1, 3, 10 and 16). After applying the criteria of selection, the study included 133 publications from 79 high-impact journals, using Web of Science (WoS) Core Collection database. Unlike previous studies, the study contributes to the existing body of knowledge by conducting systematic review of financial well-being literature from a holistic perspective and presenting the most recent and up-to-date research findings in the area. VOSviewer, a software tool was used to create bibliometric networks. The results of this systematic review study suggested the following conclusions: (a) financial well-being is a dynamic and multidimensional construct; (b) studies studying antecedents of financial well-being are far more in number than consequences; (c) majority of the previous studies are based on quantitative research methods (112), that is, secondary data research (75); (d) financial well-being has been mostly quantified using subjective measures; (e) the previous studies seems to be dominated by developed countries like the USA, Canada, Germany, and, England posing several limitations in practice; (f) Financial well-being was mostly studied with ‘poverty’, ‘behavior’, ‘income’, ‘health’ and ‘growth’. Limitations and future research directions of the current study are discussed.
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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 | 0.030 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.034 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".