Identifying Social factors that Stratify Health Opportunities and Outcomes (ISSHOOs) in pain research: consensus recommendations for the collection and reporting of equity-relevant data
Bibliographic record
Abstract
Background: The aspiration to improve health equity is fundamental to scholarly focus and action in public health, and highly relevant to addressing the global burden of pain-the leading contributor to disability worldwide. There is potential for advancement towards health equity to be facilitated by greater access to data that identifies the role of socio-demographic factors in pain and health outcomes. Methods: The 'Identifying Social factors that Stratify Health Opportunities and Outcomes (ISSHOOs) in Pain Research' project was a multi-stage process that aimed to reach consensus on the most important equity-relevant items to include in all human adult pain research. Conducted April 2022-May 2025, it incorporated two scoping reviews (published 2023), an international Delphi study (published 2025), consensus meetings and focus groups; prioritising global participation, patient perspectives, and interdisciplinary expertise throughout. Findings: Three hundred and four individuals from 45 countries, across six continents, contributed to developing two sets of items. Set A, the 'minimum dataset', is a globally relevant set of eight standardised socio-demographic items (age, sex, gender identity, place, race/ethnicity/cultural identity, education, financial position, work status), accompanied by concise guidance to assist implementation and setting-specific tailoring; Set B is an 'extended dataset' of optional items from which researchers can select items consistent with their study population and research questions. The ISSHOOs recommendations offer a culturally sensitive, cross-culturally relevant, practical and highly useful resource. Interpretation: Routine adoption and clear reporting of the ISSHOOs datasets across all human adult pain research will lead to improved and harmonious descriptions of research participants across health equity domains. Our goal is to promote equity-relevant awareness and understanding, and ultimately drive progress towards reducing avoidable disparities in health for people with pain, with potential for broader application to other fields of health. Funding: NHMRC (Australia); MAYDAY Fund; IASP; Canada Research Chair Program; European Horizon 2020 Research and Innovation Programme; ZonMw programs.
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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 | 0.767 | 0.753 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.034 | 0.030 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.025 | 0.037 |
| Open science | 0.020 | 0.040 |
| Research integrity | 0.018 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".