Impact of health system engagement on the health and well-being of people who use drugs: a realist review protocol
Bibliographic record
Abstract
Abstract Background Although community-level benefits of health system engagement (i.e., health service planning, delivery, and quality improvement, engaged research and evaluation, and collaborative advocacy) are well established, individual-level impacts on the health and well-being of community members are less explored, in particular for people who use or have used illegal drugs (PWUD). Capacity building, personal growth, reduced/safer drug use, and other positive outcomes may or may not be experienced by PWUD involved in engagement activities. Indeed, PWUD may also encounter stigma and harm when interacting with healthcare and academic structures. Our objective is to uncover why, how, and under what circumstances positive and negative health outcomes occur during health system engagement by PWUD. Methods We propose a realist review approach due to its explanatory lens. Through preliminary exploration of literature, lived experience input, and consideration of formal theories, an explanatory model was drafted. The model describes contexts, mechanisms, and health outcomes (e.g., mental health, stable/safer drug use) involved in health system engagement. The explanatory model will be tested against the literature and iteratively refined against formal theories. A participatory lens will also be used, wherein PWUD with lived experience of health system engagement will contribute throughout all stages of the review. Discussion We believe this is the first realist review to explore the contextual factors and underlying mechanisms of health outcomes for PWUD who participate in health system engagement. A thorough understanding of the relevant literature and theoretical underpinnings of this process will offer insights and recommendations to improve the engagement processes of 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 | 0.160 | 0.183 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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".