Co-creating health system innovation with people who use drugs
Bibliographic record
Abstract
BACKGROUND: A polycrisis of rising drug toxicity, pervasive houselessness, pandemic-related disruptions, coloniality and climate disasters is creating and exacerbating health inequities for People Who Use/Have Used Drugs (PWUD). This confluence of intersecting health, socio-political and environmental issues highlights the need for community-driven and adaptive innovation to address inequities in complex systems of care. To inform service innovations in an inner city social service hub in Edmonton, Alberta, we co-created a process that centres PWUD in health service planning and prioritization. METHODS: Using a community-based participatory research methodology informed by complexity theory, we conducted research with PWUD using SenseMaker micro-narratives and optional arts-based asset-mapping. Academic and peer researchers co-developed the study with input from the PWUD community and collected data at social service hubs and on outreach in the community. An iterative four-phase approach to research design, data collection and analysis guided the study: (i) Pre-data collection, (ii) Formal data collection, (iii) Readjusting, and (iv) Accountability. RESULTS: This methodology paper describes how our four-phase framework guided the study and promoted a dynamic and accountable approach to centering PWUD in health system innovation. Over five months, 215 PWUD participants shared narratives and rich insights into their experiences with healthcare access, harm reduction, and community support. Our results emphasise the importance of taking time to orient to each other and the community, even as a diverse team with many preexisting relationships. An iterative data analysis process allowed for adjustments in real-time to guide research focus, ensuring equity-oriented engagement with structurally vulnerable groups. Accountability began with research design, was maintained throughout data collection by creating safety for participants, and then defined the final phase of the research where we created an accessible final report and are now working with the host nonprofit partner and community members on action-oriented responses to the narratives shared. CONCLUSIONS: Meaningful engagement with PWUD in co-creating health system innovation requires relational and adaptive methodologies. The process-focused results of this study demonstrate how community-based participatory research informed by complexity theory can enable accountable healthcare innovation amidst a changing social and political landscape. We conclude with a set of recommendations for co-creation and other peer-centred approaches that prioritize PWUD voices in developing effective health services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".