Impact of Health System Engagement on the Health and Well-Being of People Who Use Drugs: A Realist Review
Bibliographic record
Abstract
Context People who use drugs (PWUD) choose to partner with the health system in improving care delivery for a variety of reasons. When done meaningfully, health system engagement provides mutual benefit to both communities and care teams. There may also be individual-level benefits, but also paradoxical experiences of harm; this is especially relevant for historically oppressed and equity-seeking groups. Objective To examine how health system engagement (e.g., health service planning and delivery, scholarly activities, collaborative advocacy) influences the health and well-being of PWUD. Study Design and Analysis Realist review, using the following steps: (i) clarifying scope, (ii) searching for evidence, (iii) appraising the studies and extracting data, (iv) synthesizing evidence and drawing conclusions, and (v) disseminating, implementing, and evaluating recommendations. Setting / Dataset A Western Canadian team of lived experience co-researchers, clinicians, and academic experts participated in defining the explanatory model describing the content, mechanism, and outcomes at play during PWUD-partnered activities. An Indigenous knowledge broker joined the team for the latter stages of the review at PWUD co-researchers’ recommendation based on emerging program theory. Data sources also include formal theories and 85 empiric publications selected via a librarian-facilitated search. The review scope included: Population Studied PWUD, involving any unsanctioned use of opioids/stimulants/illegal substances; Intervention Activities related to health system engagement in the form of planning, delivery, or research, whether as an internal (e.g. front line outreach work) or external (e.g. patient advisor) actor; and Outcome Measures Individual physical, emotional, social, or spiritual health and well-being. Results Healthy partnership depends on social and structural support, clear role definition, cultural safety, and an anti-oppressive space that fosters personal development. Several recommendations were generated to guide partnership best practices, including fair compensation, clear role descriptions, psychological and cultural support, continuous learning, and meaningful input into key decisions. Conclusions Multiple macro-meso-micro level conditions can create either healthy or harmful engagement experiences for PWUD. Health system actors should develop and action health-promoting policies when partnering with PWUD.
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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 | 0.027 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".