Co-Design for Developing and Integrating a Model of Care for Pain Management Centers: Protocol for a Biphase Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Current gold standard chronic pain management applies a biopsychosocial lens to clinical care, integrating medication, psychosocial support, and physical reconditioning to promote sustained treatment success, increase quality of life, and control symptoms. However, only 40% of patients with chronic pain report adequate pain management. Unfortunately, evidence describing implemented treatment pathways or models of care (MoCs) that consistently use this holistic approach is lacking. OBJECTIVE: The aim of this study is to identify the barriers and facilitators to access and engagement in existing MoCs for chronic pain, explore aspects of the delivery of care that can be improved, and develop an improved MoC for pain clinics in New South Wales by directly liaising with people with chronic pain and their families. METHODS: A 2-phase qualitative study will be conducted using semistructured focus groups. Both phases will follow the same structure, so patients, carers, and clinicians will independently provide input into the proposed MoC, which will then be summarized and integrated by the research team. Participants will be encouraged to interact and speak freely across three core domains: (1) experience of chronic pain management, (2) barriers and facilitators to delivering/accessing a gold standard MoC, and (3) key improvement points to existing models. Each focus group will last 90 minutes and be audio-recorded and transcribed verbatim for qualitative analysis. The focus groups in phase one will generate initial recommendations for primary changes to the existing MoC, and these will then be integrated and presented for evaluation, feasibility, and acceptability by stakeholder groups during phase two. RESULTS: Data collection commenced in September 2023 and is expected to end by September 2025. As of February 2024, we have completed the focus group with clinicians (n=12) and patients (n=7) in phase one. Data have not been analyzed formally and will be reported in a future publication. CONCLUSIONS: Through the exploration of key stakeholders' perspectives of the barriers preventing access and the delivery of care in the current MoC for chronic pain, we aim to co-design an appropriate, feasible, and acceptable pathway to be implemented in pain services in the future. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59126.
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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 | 0.089 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.078 | 0.013 |
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".