Advocating for Policy Change: Examples Emerging From a Medical-Legal Partnership in Primary Care
Bibliographic record
Abstract
Medical-legal partnerships bring legal services directly into clinical settings. Policy advocacy is often opportunistic and varies across partnerships. Our objective was to study policy advocacy that emerged from a medical-legal partnership in Toronto over a four-year period. This study consisted of a document review and thematic analysis, triangulated with data from interviews with legal team members and health providers. We defined policy advocacy as actions associated with attempts to change policy or legislation. The medical-legal partnership engaged in seven distinct cases of policy advocacy: disability support form requirements, changing workplace review, challenging barriers to citizenship, housing, publicly funded medication program (pharma care), safe injection sites, and the need for increased social assistance. Actions taken included presentations at conferences and submissions of briefs to government. We found that a medical-legal partnership resulted in policy advocacy with issues arising from both the health and the legal team with impacts likely greater than if each group had acted alone.
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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 | 0.056 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.052 | 0.037 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".