Supporting disability-inclusive knowledge translation and patient access to knowledge: A synthesis of select special education theories
Bibliographic record
Abstract
Equitable access to knowledge and knowledge translation that is inclusive to marginalized patients-including those whose health conditions have resulted in lifelong disability-may be supportive of health equity. In enhancing the evidence base of what constitutes disability-inclusive knowledge translation, patients may be better supported in their health literacy, self-management, or autonomy in making health-related decisions. To identify potential guiding principles from the discipline of special education that has been invested in providing equitable access to knowledge for patients living with disabilities across all age groups. Qualitative synthesis of existing theories, models, and frameworks (TMFs) in special education is performed to identify constructs which may guide disability-inclusive knowledge translation. A search methodology adapted from PRISMA-ScR was conducted in Web of Science and Scopus to identify review-type studies in special education scholarship. A total of 69 unique review-type studies were retrieved in the English language, resulting in 21 meeting the inclusion criteria of presenting a special education TMF with potential to inform knowledge translation. Ten themes emerged through data charting of theoretical constructs, as well as open coding of five studies. Findings that may promote disability-inclusive knowledge translation are presented in a synthesized framework with 25 considerations. Special education TMFs are diverse in focus; this first-steps study illustrates significant potential of special education TMFs in informing disability-inclusive knowledge translation. Future studies that engage with a more expansive set of special education TMFs will bring value to implementation science.
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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 | 0.063 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".