A consensus statement on how to conduct inclusive health research
Bibliographic record
Abstract
BACKGROUND: The active involvement of people with intellectual disabilities in research, or inclusive research, is relatively common. However, inclusive health research is less common, even though it is expected to lead to appropriate healthcare and increased quality of life. Inclusive health research can build upon lessons learned from inclusive research. METHOD: A total of 17 experts on inclusive (health) research without intellectual disabilities and 40 experts with intellectual disabilities collaborated in this consensus statement. The consensus statement was developed in three consecutive rounds: (1) an initial feedback round; (2) a roundtable discussion at the 2016 International Association for the Scientific Study of Intellectual and Developmental Disabilities World Congress; and (3) a final feedback round. RESULTS: This consensus statement provides researchers with guidelines, agreed upon by experts in the field, regarding attributes, potential outcomes, reporting and publishing, and future research directions, for designing and conducting inclusive health research. CONCLUSIONS: Consensus was reached on how to design and conduct inclusive health research. However, this statement should be continuously adapted to incorporate recent knowledge. The focus of this consensus statement is largely on inclusive health research, but the principles can also be applied to other areas.
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 | 0.382 | 0.440 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.012 | 0.022 |
| Research integrity | 0.028 | 0.037 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".