Perceptions of primary care patients attending a legal health clinic: a qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: There has been increasing recognition of the relationship between social determinants of health and unmet legal needs, with those living in poverty experiencing higher rates of poor health. Medical-legal partnerships (MLPs) are programs that have been developed to offer legal services within a clinical setting to patients who may typically not be able to access these types of services. The Legal Health Check-Up Clinic was initiated to screen and offer legal supports to patients from a Canadian primary care clinic, situated in a diverse, urban medium-sized city. Previous quantitative analyses found significant changes in overall health status as well as income, housing, and food security. The aim of this study was to evaluate participants’ experiences and satisfaction with the Legal Health Check-Up Clinic. METHODS: A qualitative descriptive approach was used and participants from the Legal Health Check-Up Clinic were invited to take part in a one-time, individual interview. Using a thematic analysis approach, each interview was independently coded and then discussed by three researchers, with any differences reconciled by consensus. Coding was iterative with new codes added as relevant ideas emerged from the interviews. RESULTS: Sixteen interviews were conducted. There were two primary themes: Personal Benefits and Challenges experienced by participants, and Program Structural Elements that fostered or impeded program success. Participants welcomed accessing legal support within the primary care environment and became more aware of their rights and options. Some learned they were not eligible for the program and felt provision of other resources would have been helpful. Participants appreciated the program philosophy of recognizing the relationship between health and social needs and offered constructive feedback for areas of improvement, such as provision of translation services and more time with clients. CONCLUSIONS: The Legal Health Check-Up Clinic was helpful for participants in providing referrals, resources, and concrete direction regarding their legal issue. Clarity in screening potential participant eligibility was suggested. Future work related to interprofessional education of legal and health professionals could support the implementation of a stronger patient-centred approach.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".