How Legal Problems Affect Health and the Role of Medical Legal Partnership in Canada
Bibliographic record
Abstract
At the moment, there is little research involving the connection between legal issues and an individual’s overall health. It makes sense that non-individual factors go into determining one’s health, such as physical environment, housing, and education. These factors are typically referred to as the Social Determinants of Health (SDOH) (Mikkonen and Raphael 2012). However, at times, individuals experience issues that are legal in nature, which can in many ways affect one’s health. It is this recognition that has sparked the creation of medical-legal partnerships throughout the United States, and more recently in Canada. The health benefits of medical-legal partnerships are well documented in the United States and have created a network of medical-legal organizations, in addition to new practices in both legal and medical education (Theiss 2017; Tobin-Tyler 2011). This research explores the establishment of one of Canada’s first Medical-Legal Partnerships (MLP) between The University of Calgary’s Student Legal Assistance (SLA) and Calgary’s downtown community health center (CUPS). This research explores how legal supports included alongside healthcare service provision can work to address structural inequalities that exist in our social environment and are created or exacerbated by one’s “legal wellbeing". Interviews with healthcare and social service providers working alongside the CUPS and SLA MLP provides novel insight into the various ways legal issues can disrupt access to healthcare, interrupt health-seeking behaviours, or create unique health crises all on their own. The idea behind this is to prove that legal wellbeing is a distinct social determinant of health. The following research aims to establish a foundation for the further development of Medical-Legal Partnership throughout Canada and challenges readers to ask, what does access to justice really look like?
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.037 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".