Challenges and best practices for recruiting families of children with intellectual disabilities for health research
Bibliographic record
Abstract
Research focused on children with intellectual disabilities has been of increasing interest over the last two decades. However, a considerable lag in the amount of research that is representative and generalizable to this population in comparison to neurotypical children remains, largely attributed to issues with participant engagement and recruitment. Challenges and barriers associated with engaging and recruiting this population include lack of research to provide a sound foundation of knowledge, ethical considerations, parental attitudes, family commitments, and organizational gatekeeping. Researchers can engage children and their families using participatory research methods, honouring the child's right to assent, and collaborating with parents. Recruitment strategies include partnering with organizations, working with parent and patient partners, and using remote methods. Employing evidence-informed engagement and recruitment strategies may provide substantial social and scientific value to the research field by ensuring that this underrepresented population benefits equitably from research findings.
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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 | 0.510 | 0.578 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.016 | 0.023 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".