Assessment of Stakeholder Engagement in a Down Syndrome Research Study
Bibliographic record
Abstract
There is growing recognition of the importance of engaging patients early in the design of research studies. For studies involving patients with intellectual and cognitive disabilities, researchers may consider engaging with family caregivers, health professionals, community advocates, and/or subject matter experts to provide a more multifaceted, surrogate perspective. Evaluating the engagement of these stakeholder groups in research is nascent, and tools are limited. Research studies involving these individuals provide the opportunity to test new methods of measurement of stakeholder engagement in research. We conducted a 3-year research study implementing and evaluating Down Syndrome Clinic to You, an online platform for caregivers of individuals with Down syndrome who do not have access to Down syndrome specialists. We established 3 key stakeholder groups - family caregivers, primary care physicians, and medical/scientific experts in the field - who were involved from grant-writing through preparation of the final report. To assess stakeholder engagement, we utilized the Patient Engagement in Research Scale, a validated instrument originally developed to evaluate patient engagement in arthritis research. Overall, results were suggestive of strong engagement levels by the key stakeholder groups. This study contributes to the limited available literature evaluating measures of stakeholder engagement in research.
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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 | 0.329 | 0.290 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".