Co-Design of a Digital Health Tool for Use by Individuals With Opioid Use Disorder: App4Independence (A4i-O)
Bibliographic record
Abstract
BACKGROUND: Opioid use disorder (OUD) has arguably the highest mortality rate of mental health conditions; opiate-related deaths are identified as the number one cause of accidental deaths in Canada and the United States. Specialized care for OUD is often described as lacking, fractured, and with frequent periods of disengagement. Digital health strategies may support connection to evidence-based resources even during periods of disengagement. However, sustained engagement in digital interventions remains a barrier, and as such, experts recommend using co-design approaches to develop interventions. METHODS: The current study outlines the results from a qualitative co-design project that engaged 6 lived experts and 8 clinical experts in a series of focus groups and interviews to adapt an existing intervention for use in OUD. Focus groups and interviews were recorded and transcribed before undergoing thematic analysis. This co-design process is the first stage of a larger project that will lead to the development of a novel digital health intervention for OUD populations. RESULTS: Transcripts underwent thematic analysis, and themes were divided into Crosscutting Themes, Feasibility and Engagement, and Specific Features. Each theme was divided into specific subthemes, which were reviewed by the design team and informed the design of the digital health platform. Key resulting directions included creating a psychologically safe digital space, curating resources for OUD as a multifaceted condition, and being mindful of barriers to implementation from both lived and clinical expert perspectives. Specific features are discussed in detail in the article. CONCLUSION: Lived experts and clinicians strongly supported integrating digital tools into OUD care. Ongoing work is needed to better understand the role of technology in existing OUD structures as well as the implementation of key features such as digital peer support and creating effective and safe social connections. This study also validates co-design as an essential step in digital health development.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".