Developing consensus on the most important equity-relevant items to include in pain research: a modified e-Delphi study
Bibliographic record
Abstract
ABSTRACT: There is increasing recognition of the need for routine measurement and reporting of data that can reveal social factors that contribute to health inequities for people with pain. Prioritising what data to collect and understanding how to collect it can be challenging, and no clear guidance exists. We conducted a 3-round Delphi study to develop consensus on the most important items to include in a minimum dataset of equity-relevant variables. An international panel of experts and interest-holders were invited to participate based on expertise in pain, social determinants of health and health equity, or a lived experience of persistent pain. In round 1, 168 participants rated the importance of an initial set of 43 equity-relevant items and categorised them according to the PROGRESS-Plus Framework. Twenty-nine items reached agreement for inclusion (based on a threshold of panel median of ≥7 of 9); none of the items were excluded. Participant comments were collated, and 21 new items were proposed. In round 2, 152 participants (90% of round 1) voted on 35 items, 25 of which reached agreement for inclusion. In round 3, 142 participants (93% of round 2) prioritised the 54 items that reached the threshold for inclusion within each category and rated the PROGRESS-Plus category importance. Our results indicated consistent agreement that it is important to collect data on a wide range of social factors and provide rich data to inform the development of a consensus-derived, globally applicable, "minimum dataset" that will be recommended for routine use in all human pain research.
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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 | 0.247 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".