Researching what matters to improve chronic pain care in Canada: A priority-setting partnership process to support patient-oriented research
Bibliographic record
Abstract
BACKGROUND: Chronic pain affects more than 6 million Canadians. Patients need to be involved in setting research priorities to ensure a focus on areas important to those who will be most impacted by the results. AIMS: The aim of this study was to leverage patient experiences to identify chronic pain research priorities in Canada. METHOD: The process was informed by the James Lind Alliance. After gathering an exhaustive list of questions using surveys, town hall meetings, interviews, and social media consultations, we used a computerized Delphi with four successive iterations to select the final list of research priorities. The final Delphi round was conducted by a panel of ten patients living with chronic pain and ten clinicians from different disciplines. RESULTS: We received more than 5000 suggestions from 1500 people. The Delphi process led to the identification of 14 questions fitting under the following 4 themes: (1) improving knowledge and competencies in chronic pain; (2) improving patient-centered chronic pain care; (3) preventing chronic pain and reducing associated symptoms; and (4) improving access to and coordination of patient-centered chronic pain care. Challenges included the issue of chronic pain being ubiquitous to many diseases, leading to many initial suggestions focusing on these diseases. We also identified the need for further engagement efforts with marginalized groups in order to validate the priorities identified or identify different sets of priorities specific to these groups. CONCLUSION: The priorities identified can guide patient-oriented chronic pain research to ultimately improve the care offered to people living with chronic pain.
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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 | 0.193 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.035 | 0.013 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".