Development of recommendations for a minimum dataset for Identifying Social factors that Stratify Health Opportunities and Outcomes (ISSHOOs) in pain research
Bibliographic record
Abstract
There is increasing recognition of the need for researchers to collect and report data that can illuminate health inequities. In pain research, routinely collecting equity-relevant data has the potential to inform about the generalisability of findings; whether the intervention has differential effects across strata of society; or it could be used to guide population targeting for clinical studies. Developing clarity and consensus on what data should be collected and how to collect it is required to prompt researchers to further consider equity issues in the planning, conduct, interpretation, and reporting of research. The overarching aim of the ‘Identifying Social Factors that Stratify Health Opportunities and Outcomes’ (ISSHOOs) in pain research project is to provide researchers in the pain field with recommendations to guide the routine collection of equity-relevant data. The design of this project is consistent with the methods outlined in the ‘Guidance for Developers of Health Research Reporting Guidelines’ and involves 4 stages: (i) Scoping review; (ii) Delphi Study; (iii) Consensus Meeting; and (iv) Focus Groups. This stakeholder-engaged project will produce a minimum dataset that has global, expert consensus. Results will be disseminated along with explanation and elaboration as a crucial step towards facilitating future action to address avoidable disparities in pain outcomes. A graphical abstract is attached with this submission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.482 | 0.708 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.016 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.012 | 0.019 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.045 | 0.017 |
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".