Workshop (Clinical/Best Practice Implementation) ID 1998666
Bibliographic record
Abstract
Background Persons with lived experience of SCI (PLEX) living in rural areas have difficulty accessing optimal care and are required to travel long distances to access specialized services. Clinicians not affiliated with specialized urban centres recognize that developing knowledge and skills specific to SCI will improve care outcomes. Praxis has conducted multiple workshops on SCI topics throughout the BC Interior Health Region to improve clinician knowledge about SCI. Topics include pressure injuries, autonomic dysreflexia, specialized equipment, bowel and bladder, etc. Utilizing evidence-based information and PLEX experiences, these workshops have increased clinician confidence when working with clients with SCI. This interactive workshop explores the development and implementation of the sessions, from the perspectives of a clinician, a PLEX, and a knowledge exchange expert. Objectives The goals of this workshop are to: Describe the creation of rural workshops including the stakeholder engagement process of both clinicians and PLEX. Demonstrate how SCI Workshops delivered by PLEX and clinical experts result in increased awareness, knowledge, and confidence in working with PLEX for clinicians in rural areas. Engage workshop participants to share learnings from other initiatives aimed at improving knowledge and confidence of clinicians working with PLEX in rural areas. Conclusion This workshop demonstrates the benefit of SCI knowledge exchange for clinicians working in rural areas. Furthermore, it highlights key factors in developing these workshops, and provides an opportunity to connect with other rural SCI initiatives and networks aimed at enhancing the care and well-being of PLEX.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.352 | 0.101 |
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".