Poster (Clinical/Best Practice Implementation) ID 1985364
Bibliographic record
Abstract
Background/objectives The engagement of people with lived experience (PLEX) of spinal cord injury/disease (SCI/D) in rehabilitation research can lead to relevant questions and improved data collection, interpretation, knowledge translation, and research impact. We describe the process to create a toolkit which elaborates the roles that PLEX can play in rehabilitation research to ensure engagement is authentic and effective. Methods Five separate working groups were convened to each focus on a specific role of PLEX: research team member, peer reviewer, knowledge translator, decision-maker, and fundraising ambassador. The roles of PLEX in research, relevant training tools, and indicators to measure engagement were explored through 17 virtual meetings with 45 scientists, research staff, learners, and PLEX. Menti-meter and Survey Monkey were used to select training tools via consensus. A summative meeting was held with all participants to achieve consensus regarding the role descriptions. Meeting transcripts and survey data informed iterations of the materials prior to achieving consensus. Findings The Toolkit contains five role descriptions for PLEX as well as example activities, training requirements for scientists and PLEX, and specific indicators for each role. The Toolkit includes several best practice considerations and three practical tools for researchers to plan engagement, facilitate compensation, and implement/evaluate engagement. Conclusions The Toolkit can be used by researchers and research organizations to develop, implement, and evaluate engagement plans with PLEX in SCI/D rehabilitation research. This Toolkit can be used to transform the SCI/D rehabilitation research and advocacy agenda, and contribute to more relevant research with a greater impact.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.849 | 0.530 |
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".