Effect of a Legal Clinic Program Within an Urban Primary Health Care Center on Social Determinants of Health: A Program Evaluation
Bibliographic record
Abstract
Background: Individuals living in poverty often visit primary healthcare clinics for health problems stemming from unmet legal needs. We examined the impact of a medical-legal partnership on improving the social determinants of health (SDoH), health-related quality of life, and perceived health status of attendees of a Legal Clinic Program (LCP). Methods: This was a pre-post program evaluation of a weekly LCP established within an urban primary healthcare clinic to provide free legal consultation. Patients aged 18 years or older were either approached or referred to complete a screening tool to identify potential legal needs. Those identified with potential legal needs were offered an appointment with LCP lawyers who provided legal counsel, referrals, and services. For those who attended the LCP, changes in SDoH and health indicators were collected via a self-reported survey 6 months after they attended the LCP and compared to their baseline scores using paired t-tests, McNemar’s test for paired proportions, and the Wilcoxon Signed Rank Test for related samples. Results: During the 6-month evaluation period, 31 participants attended the LCP and completed both the baseline and 6-month surveys; 67.8% were female, 64.5% were white, 90.3% were not working full-time, and 61.3% had a household income of $700 to 1800 per month. At follow-up, 25.8% were receiving at least 1 new benefit and there was a statistically significant reduction in food insecurity (35.5% vs 9.7%, P < .05). Also, perceived health status using the visual analog scale (ranges from 0 to 100) significantly improved from 42.5 points (SD = 25.3) at baseline to 56.6 points (SD = 19.6) after 6 months ( P < .05). Conclusions: The LCP has the potential to improve the health and wellbeing of patients in primary healthcare clinics by addressing unmet legal needs and SDoH.
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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 | 0.021 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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".