Considerations when asking about “disability” in disability inclusive research
Bibliographic record
Abstract
PURPOSE: There are several ways to include "disability" in research studies, which can be confusing or overwhelming for researchers, community members, and students. The aim of this paper is to share conceptualizations of disability and how to ask about "disability" in research studies. The paper provides a general introduction and brief analysis of the methodological approaches which can be used. METHODS: We used reviews of the literature and extensive discussions to identify key articles, books, websites, and reports that provide guidance and examples of asking about disability in research. RESULTS: Four primary approaches to asking study participants about disability were identified. For each of these, we provide background information, key points about the ways to use the approach including tools that have been developed, and example studies. A comparison table provides a high-level overview of similarities and differences in approaches. Other approaches and tools were also identified and are briefly described. CONCLUSION: Researchers involved in disability and rehabilitation research should be aware that there is not one best or singular way to ask about disability when conducting research. The approach or approaches chosen for a particular study need to match the purpose of the study. It is important that researchers take time to carefully consider their options and choose the best fit for their study.
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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 | 0.348 | 0.370 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.020 | 0.042 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".