ArticleSystematic reviews2025
Medical-Legal Partnerships for women in the United States: a systematic review protocol.
Article in Systematic reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundMLPs offer a promising approach to addressing needs related to adverse social determinants of health through the provision of legal services in clinical healthcare settings. Evidence to support the MLP model is in early stages. Little is known about the gendered impacts of the MLP model or how they might be tailored for women. The objectives of this systematic review are to (1) compare the impacts of Medical-Legal Partnerships (MLPs) by gender, and (2) identify tailored approaches adopted by MLPs to assist women.
methodsThis protocol conforms to the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Protocol) Statement. MEDLINE, Embase, PsycInfo, Web of Science, Scopus, NBER, LexisUni, HeinOnline, and the National Center for Medical Legal Partnerships web site will be searched for English-language manuscripts published after 1993 (the year attributed to the first MLP). For Objective 1, we will apply the PICO (Population, Intervention, Comparators, Outcomes) Framework to determine eligibility, focusing on peer-reviewed articles that describe MLPs in the U.S. (Intervention) that serve women (Population) and present evidence on MLP's interpersonal-, institutional-, community-, and policy-level impacts (Outcomes) by gender (Comparators). To evaluate bias in Objective 1 articles, we will use the Risk-of-Bias 2 tool for randomized controlled trials and the Risk of Bias in Non-Randomized Studies of Interventions for non-randomized studies. For Objective 2, we will apply the PICo (Population, phenomena of Interest, Context) framework, including peer-reviewed and gray literature that describes tailored approaches (phenomena of Interest) to providing legal support to women or females (Population) in U.S. clinical settings (Context). We will use the Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research to assess possible bias for Objective 2 articles. Two reviewers will independently screen titles, abstracts, and full texts, meeting regularly to discuss conflicts and establish consensus. Data from selected publications will be extracted and entered in a matrix. Findings will be presented in narrative and tabular forms. DISCUSSION: This review will provide invaluable evidence on the impact of MLPs by gender and will provide insights into future research and interventions on MLPs serving populations of women. SYSTEMATIC REVIEW REGISTRATION: Open Science Framework: https://osf.io/wd6t5/?view_only=2d6baf14c7b2408293ac31ee5921c8e3.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.