Trauma-Informed Financial Empowerment Programming Improves Food Security Among Families With Young Children
Bibliographic record
Abstract
OBJECTIVE: To determine how trauma-informed programming affects household food insecurity (HFI) over 12 months. DESIGN: Change was assessed in HFI from baseline to 12 months in response to a single-arm cohort intervention. Measures were taken at baseline and in every quarter. Two participant groups were compared: participation in ≥4 sessions (full participation) vs participation in <4 sessions (low/no participation). SETTING: Community-based setting in Philadelphia, Pennsylvania. PARTICIPANTS: A total of 372 parents of children aged <6 years, participating in Temporary Assistance for Needy Families and the Supplemental Nutrition Assistance Program, recruited from county assistance offices and community-based settings. INTERVENTION: Trauma-informed programming incorporates healing-centered approaches to address previous exposures to trauma. Sixteen sessions addressed emotional management, social and family dynamics related to violence exposure and childhood adversity, and financial skills. MAIN OUTCOME MEASURES: Household food insecurity, as defined by the US Department of Agriculture Household Food Security Survey Module. ANALYSIS: Mixed-effects logistic regression models were used to compare groups from baseline to 12 months, controlling for adverse childhood experiences, depression, and public assistance. RESULTS: Those with full participation had 55% lower odds of facing HFI compared with the low/no participation group (adjusted odds ratio = 0.45; 95% confidence interval, 0.22-0.90). CONCLUSIONS AND IMPLICATIONS: Trauma-informed programming can reduce the odds of HFI and may reduce trauma-related symptoms associated with depression and poverty.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".