Applying Patient and Health Professional Preferences in Co-Designing a Digital Brief Intervention to Reduce the Risk of Prescription Opioid–Related Harm Among Patients With Chronic Noncancer Pain: Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: Few personalized behavioral treatments are available to reduce the risk of prescription opioid-related harm among patients with chronic noncancer pain. OBJECTIVE: We aimed to report on the second phase of the co-design of a digital brief intervention (BI) based on patient and health professional preferences. METHODS: Eligible patients with chronic noncancer pain (n=18; 10 women; mean age 49.5, SD 6.91 y) from public hospital waitlists and health professionals (n=5; 2 women; mean age 40.2, SD 5.97 y) from pain and addiction clinics completed semistructured telephone interviews or participated in focus groups exploring BI preferences, needs, and considerations for implementation. Grounded theory was used to thematically analyze the data. RESULTS: We identified 5 themes related to intervention content from patient reports: relevance of the biopsychosocial model and need for improved awareness and pain psychology education; nonpharmacological strategies and flexibility when applying coping skills training; opioid use reflection and education, with personalized medication and tapering plans; holistic and patient-inclusive assessment measures and feedback; and inclusion of holistic goals targeting comfort and happiness. Five themes related to the process and guiding principles were identified: therapist guided; engaging features; compassionate, responsive, person-centered care; a digital solution is exciting, maximizing reach; and educate and normalize system and policy challenges. Finally, 5 themes were reflected in the health professionals' reports: digital health use is rare but desired; digital health is useful for patient monitoring and accessing support; patient motivation is important; a digital BI app is likely beneficial and at multiple care points; and safe medication use and managing pain goals. The reported barriers from health professionals were intervention intensity, potential costs, and patient responsiveness; factors facilitating the implementation were the alignment of digital BIs with clinical models, a stepped-care approach, and feedback. CONCLUSIONS: This co-design study identified key content areas, guiding principles, enabling factors, and barriers from both patients and health professionals to guide the development of digital BIs. The knowledge gathered should inform future iterations of co-designing digital BIs for the population most at risk of the harmful effects of opioid medications.
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 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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".