Overcoming structural inequity in healthcare for people living with substance use issues and mental illness.
Bibliographic record
Abstract
Structural stigma arbitrarily limits the opportunities and/or constrains the right of persons with lived and living experience of mental health problems and illnesses and/or substance use (PWLLE). While it often occurs unknowingly, it creates inequities embedded in the fabric of our social institutions, organizations, and our shared ways of thinking and acting. In health care, structural stigma exists in the laws, policies, practices, and models of care that deprioritize, dehumanize, and fail to treat PWLLE as equitably as those with physical health concerns. Such unfairness leads to overall poorer healthcare access, less availability of evidence-based services, and lower quality of care for these individuals, whether their concerns relate to physical health, mental health, and/or substance use. In 2019, the Mental Health Commission of Canada (MHCC) set out on a multi-year project to examine structural stigma in health-care settings and develop tools and approaches for dismantling it. Through this work, the MHCC has published a literature review, produced a conceptual framework for dismantling structural stigma in health systems, and co-developed a measure with PWLLE to assess structural stigma in different healthcare settings and contexts. A major part of this work includes engaging with PWLLE and health leaders to better understand the priorities and strategies for successfully addressing structural stigma within the health system. The presentation will showcase one such engagement project, known as the Champions and Changemakers project. The goal was to learn and work with champions within the Canadian health system (health providers, leaders, and decision makers, PWLLE, other stakeholders) who were implementing innovations to enhance the equity of care for PWLLE- including changes to service delivery, models of care, training models, as well as policy, advocacy and leadership and power-sharing structures. Through this collaboration process, we expanded knowledge of the key ingredients and processes for structural change (features, strategies, context, mechanisms, outcomes), and co-designed an implementation guide that can be used to provide guidance to others interested in reducing structural stigma within their own organizations. The collaboration and co-design process involved several group and one-on-one interviews and stakeholder meetings, facilitated participatory workshops with all stakeholders, and ongoing input, feedback, and review from all stakeholders in the building of the guide. In addition to the knowledge products and relationship building that came from this project, the other main impact is that the MHCC is now working to build a sustainable community of practice to promote and support ongoing work and efforts in addressing structural stigma in the Canadian healthcare context. Learning Objectives: Describe and understand Structural Stigma and how it creates inequity in healthcare for PWLLE; Demonstrate an understanding of key elements of select promising practices that support improved access to- and quality of- health care for PWLLE; Identify opportunities to disrupt and dismantle structural stigma, and; Understand the next steps to continue to address mental health and substance use-related structural stigma in health-care settings. Audience: Health-care leaders, administrators, providers, policymakers, people with lived and living experience and their caregivers and/or family of choice.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".