Workshop (Clinical/Best Practice Implementation) ID 1972517
Bibliographic record
Abstract
Background/Objectives Teams with diverse perspectives and experiences have been increasingly recognized for their ability to identify key issues and utilize creativity and problem solving to plan and implement research and clinical initiatives that lead to greater impact. Yet, often we are unsure how to best engage individuals with differing expertise, such as those with lived experience, clinicians, healthcare administrators, engineers, researchers, funders and policy experts. After completing this workshop, participants will be able to: 1) Understand the principles and value of meaningfully engaging with a diverse team when conducting research or clinical projects, 2) Identify strategies that can facilitate the meaningful engagement of individuals with differing expertise, and 3) Create a plan of engagement for a research study or clinical initiative. Methods/Overview A combination of lecture-based and case-based learning will be used to explain the guiding principles and best-practices for meaningful engagement and to discuss relevant resources, such as the North American Spinal Cord Injury (SCI) Consortium’s SCI Resource Advocacy Course and the Integrated Knowledge Translation Guiding Principles. Participants will be asked to apply these principles and practices to one of their own research or clinical initiatives through small group discussion. The Canadian Activity-Based Therapy (ABT) Community of Practice, which brings together diverse groups to address priorities for ABT research and clinical care, will be used as a case example during large group discussion. Results Not applicable. Conclusions Through this workshop, participants will gain knowledge and strategies that can be applied to facilitate meaningful engagement in research and clinical initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.507 | 0.215 |
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".