Exploring the Link Between Financial Health Indicators: Insights from Perception, Lived Experiences and Financial Resilience: A Study on Employees of a Sugar Mill Company
Bibliographic record
Abstract
The purpose of this study was to assess workers’ perceptions, experiences, and strategies related to financial health, with the goal of identifying and validating a model of financial resilience aligned with theoretical and empirical fit criteria. A sequential quantitative approach was employed, combining exploratory factor analysis (EFA) to uncover latent dimensions, followed by confirmatory factor analysis (CFA) to validate the resulting structure. This dual methodology was designed to ensure both empirical robustness and theoretical coherence. The study used a non-experimental, cross-sectional design and drew on survey data from 311 employees of a sugar company in San Juan Bautista Tuxtepec, Oaxaca, selected through a non-probabilistic self-selection sampling method. The instrument, based on existing models was administered electronically. Internal consistency was assessed using Cronbach’s alpha (α), McDonald’s omega (ω), composite reliability (CR), and average variance extracted (AVE), while multivariate normality was also examined. Findings reveal that financial resilience encompasses not only recovery from financial shocks but also proactive financial behaviors such as budgeting, long-term saving, and responsible debt management. Respondents emphasized the role of credit history, insurance access, and perceived financial autonomy in promoting both financial stability and emotional well-being. These results contribute to the theoretical conceptualization of financial resilience and have practical implications for policy and financial education with a preventive and mental health-oriented perspective.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".