Implementing Lived Experience Workshops in Regional Areas of British Columbia to Enhance Clinicians’ Confidence in Spinal Cord Injury Care: An Evaluation
Bibliographic record
Abstract
In British Columbia (BC), there are challenges accessing specialized spinal cord injury care and resources. This paper evaluated the impact of spinal cord injury health educational workshops delivered in regional communities that were informed by persons with lived experience. A community survey was conducted with 44 persons with lived experience in a BC region to identify priority SCI health-related topics. Twenty-five topics were ranked from 1-14, with bowel and bladder management ranked 1 and 4, sexual health ranked 5, and pressure injuries ranked 7. Clinical perspectives on the priorities were collected from 102 clinicians in the BC region, who independently ranked 14 of these SCI topics and considered the former 4 topics to be lower clinical priority (ranked 11-14). These priorities informed a series of SCI clinical education workshops held at healthcare facilities in three regional cities. The goals were to improve clinicians' knowledge and confidence levels when managing spinal cord injury health and to facilitate person-centred care. Positive feedback demonstrated that educational workshops supported by lived experience perspectives effectively enhanced the clinicians' understanding of spinal cord injury and their priorities. Future plans include engaging more administrators as part of this initiative and conducting workshops in other regions of BC.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".