Legal needs of patients attending an urban family practice in Hamilton, Ontario, Canada: an observational study of a legal health clinic
Bibliographic record
Abstract
BACKGROUND: Individuals living in poverty often visit their primary care physician for health problems resulting from unmet legal needs. Providing legal services for those in need may therefore improve health outcomes. Poverty is a social determinant of health. Impoverished areas tend to have poor health outcomes, with higher rates of mental illness, chronic disease, and comorbidity. This study reports on a medical-legal collaboration delivered in a healthcare setting between health professionals and lawyers as a novel way to approach the inaccessibility of legal services for those in need. METHODS: In this observational study, patients aged 18 or older were either approached or referred to complete a screening tool to identify areas of concern. Patients deemed to have a legal problem were offered an appointment at the Legal Health Clinic, where lawyers provided legal advice, referrals, and services for patients of the physicians. Fisher's exact test was used to compare populations. Binary logistic regression was used to determine the factors predicting booking an appointment with the clinic. RESULTS: Eighty-four percent (n = 648) of the 770 patients screened had unmet legal needs and could benefit from the intervention, with an average of 3.44 (SD = 3.42) legal needs per patient screened. Patients with legal needs had significantly higher odds of attending the Legal Health Clinic if they were an ethnicity that was not white (OR = 2.48; 95% CI 1.14-5.39), did not have Canadian citizenship (OR = 4.40; 95% CI 1.48-13.07), had housing insecurity (OR = 3.33; 95% CI 1.53-7.24), and had difficulty performing their usual activities (OR = 2.83; 95% CI 1.08-7.43). As a result of the clinic consultations, 58.0% (n = 40) were referred to either Legal Aid Ontario or Hamilton Community Legal Clinic, 21.74% (n = 15) were referred to a private lawyer; one case was taken on by the clinic lawyer. CONCLUSION: The Legal Health Clinic was found to fulfill unmet legal needs which were abundant in this urban family practice. This has important implications for the future health of patients and clinical practice. Utilizing a Legal Health Clinic could translate into improved health outcomes for patients by helping overcome barriers in accessing legal services and addressing social causes of adverse health outcomes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".