Co-Designing a Digital Brief Intervention to Reduce the Risk of Prescription Opioid–Related Harm Among People With Chronic Noncancer Pain: Qualitative Analysis of Patient Lived Experiences
Bibliographic record
Abstract
BACKGROUND: Opioid medications are important for pain management, but many patients progress to unsafe medication use. With few personalized and accessible behavioral treatment options to reduce potential opioid-related harm, new and innovative patient-centered approaches are urgently needed to fill this gap. OBJECTIVE: This study involved the first phase of co-designing a digital brief intervention to reduce the risk of opioid-related harm by investigating the lived experience of chronic noncancer pain (CNCP) in treatment-seeking patients, with a particular focus on opioid therapy experiences. METHODS: Eligible patients were those aged between 18 and 70 years with CNCP at a clinically significant level of intensity (a score of ≥4 of 10). Purposive sampling was used to engage patients on public hospital waitlists via mail or through the treating medical specialist. Participants (N=18; n=10 women; mean age 49.5 years, SD 11.50) completed semistructured telephone interviews. Interviews were transcribed verbatim, thematically analyzed using grounded theory, and member checked by patients. RESULTS: Eight overarching themes were found, listed in the order of their prominence from most to least prominent: limited treatment collaboration and partnership; limited biopsychosocial understanding of pain; continued opioid use when benefits do not outweigh harms; a trial-and-error approach to opioid use; cycles of hopefulness and hopelessness; diagnostic uncertainty; significant negative impacts tied to loss; and complexity of pain and opioid use journeys. CONCLUSIONS: The findings of this study advance progress in co-designing digital brief interventions by actively engaging patient partners in their lived experiences of chronic pain and use of prescription opioid medications. The key recommendations proposed should guide the development of personalized solutions to address the complex care needs of patients with CNCP.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".