Participatory research: a Priority Setting Partnership for chronic musculoskeletal pain in Denmark
Bibliographic record
Abstract
OBJECTIVES: Patient and stakeholder engagements in research have increasingly gained attention in healthcare and healthcare-related research. A common and rigorous approach to establish research priorities based on input from people and stakeholders is the James Lind Alliance Priority Setting Partnership (JLA-PSP). The aim of this study was to establish research priorities for chronic musculoskeletal (MSK) pain by engaging with people living with chronic MSK pain, relatives to people living with chronic MSK pain, healthcare professionals (HCP), and researchers working with chronic MSK pain. METHODS: This JLA-PSP included a nation-wide survey in Denmark, an interim prioritisation, and an online consensus building workshop. The information gained from this was the basis for developing the final list of specific research priorities within chronic MSK pain. RESULTS: In the initial survey, 1010 respondents (91% people living with chronic MSK pain/relatives, 9% HCPs/researchers) submitted 3121 potential questions. These were summarised into 19 main themes and 36 sub-themes. In the interim prioritisation exercise, 51% people living with pain/relatives and 49% HCPs/researchers reduced the list to 33 research questions prior to the final priority setting workshop. 23 participants attended the online workshop (12 people/relatives, 10 HCPs, and 1 researcher) who reached consensus for the most important research priorities after two rounds of discussion of each question. CONCLUSIONS: This study identified several specific research questions generated by people living with chronic MSK pain, relatives, HCPs, and researchers. The stakeholders proposed prioritization of the healthcare system's ability to support patients, focus on developing coherent pathways between sectors and education for both patients and HCP. These research questions can form the basis for future studies, funders, and be used to align research with end-users' priorities.
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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 | 0.119 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".