Conceptualizing interprofessional working – when a lawyer joins the healthcare mix
Bibliographic record
Abstract
Research, policy and practice in the field of interprofessional collaboration have focused on how medical, nursing, allied health and social care practitioners work together to positively impact patient care. This paper extends conceptual thinking about interprofessional practice by focusing on lawyers as part of the interprofessional mix. This attention is prompted by medical–legal partnerships (MLPs), a service model by which lawyers join health care settings to assist patients with unmet, and often health-harming, legal needs. MLPs are present in around 450 hospitals and other health care sites across the United States and the model has spread to other countries, including Australia, the United Kingdom and Canada. However, enthusiasm for the MLP model is not yet matched by good evidence on how, when and for whom the model works. Interprofessional scholars contend that imprecise terminology and poor conceptualization of interprofessional arrangements hinder high-quality research and evaluation. In response to their critiques, this paper formulates a stepwise conceptual framework to guide the design, implementation and study of interprofessional arrangements that connect health, social care and legal practitioners. This framework draws on findings from national surveys of MLP initiatives in several countries and adapts several key conceptual frameworks that have been developed from systematic reviews of interprofessional working in primary health care. These conceptual frameworks are valuable because they promote clarity about different modes of interprofessional working and characterize the factors at macro (policy, funding), meso (organizational) and micro (practitioner, patient) levels that help or hinder professionals from different disciplines in working together. The paper considers factors at these three levels that require particular attention when lawyers join health care settings and proposes questions for future research in this emerging area.
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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 | 0.031 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.016 | 0.065 |
| Scholarly communication | 0.023 | 0.036 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".