The Impact of Payday Loans on Healthcare Choices & Physical Health
Bibliographic record
Abstract
Healthcare economics and payday loans have been a significant challenge for the United States population for many years, with various papers finding broad connections between them. This paper focuses on one such connection: how payday loans impact the overall physical health of an individual. I have analyzed data from the National Longitudinal Surveys of Youth and the Payday Loans and Customer Service data source from Kaggle, and estimated regression models in order to find correlations between variables. Using this, I have found a direct connection between payday loans and healthcare choices: if people take out payday loans, they are more likely to be in a better state of physical health. Also, taking out a payday loan does not necessarily result in a visit to the doctor's office. In additional analysis, I find that racial minorities are most likely to take out payday loans. Lastly, I have highlighted the need for higher transparency around the fees/interest charged on payday loans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".