Impact of Medical Legal Partnerships: A Scoping Review
Bibliographic record
Abstract
Background Medical Legal Partnerships (MLPs) are collaborations between healthcare and legal services that aim to address the health-harming impacts of unmet legal needs. Better characterization of existing MLP models would be a resource for new and expanding MLPs to glean insight into challenges and opportunities to consider. This scoping review aimed to examine and map outcomes reported by MLPs. Methods MEDLINE, EMBASE, CINAHL, and the Index to Legal Periodicals databases were searched and studies reporting qualitative or quantitative outcomes of a MLP were eligible for inclusion. Independent dual review of titles, abstracts, and full-texts was conducted and the reported outcomes were analyzed. Results Thirty studies met inclusion criteria. Children and families were the most commonly served populations. The most frequently addressed legal needs pertained to housing, income, and personal/family stability. MLPs were associated with improved health, health services use, and legal outcomes. Education of healthcare professionals was associated with increased knowledge and confidence in addressing social needs. Discussion Overall, MLPs effectively partner healthcare and legal services to mitigate the health-harming consequences of unmet legal needs. MLPs facilitate access to care in legal circumstances that would otherwise exacerbate health conditions, and largely benefit communities that have been historically underserved by medical and legal systems.
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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 | 0.018 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".