Inflation hardship, gender, and mental health
Bibliographic record
Abstract
Inflation hit a 40 year high in the United States in 2022, yet the impact of inflation related hardships on distress is poorly understood, particularly the impact on women, whose income is already more limited. Using data from the US Household Pulse Survey (September-November 2022), we test whether exposure to inflation hardships is associated with greater distress and whether this association is moderated by gender (n = 119,531). We draw on a list of eighteen inflation related hardships (e.g., purchasing less food, working additional jobs, delaying medical treatment) to construct an ordinal measure of exposure to inflation hardship ranging from "no inflation hardship" to "five or more inflation hardships." We observe that an increasing number of inflation hardships is associated with higher levels of distress. We find no evidence of gender differences in the magnitude of that association at lower levels of inflation hardship (four inflation hardships or less). However, our findings suggest that exposure to five or more inflation hardships is more strongly associated with distress among men compared to women. The current study provides new insights into the cumulative burden of inflation hardships on mental health and the role that gender plays in this association.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".