SCI-IEQCC Network and SCI-Ontario: Working together to develop and implement a skin check video resource for pressure injury prevention in spinal cord injury
Bibliographic record
Abstract
Individuals with Spinal Cord Injury/Disease (SCI/D) face a high risk of developing pressure injuries (PI), which can significantly impact their health, well-being and the economic burden on the health care system. To help mitigate these risks, the Spinal Cord Injury Implementation and Evaluation Quality Care Consortium (SCI-IEQCC) identified gaps during the implementation of indicators in the tissue integrity domain, specifically in the area of patient education regarding daily skin checks. A collaborative effort involving SCI-IEQCC, Spinal Cord Injury Ontario (SCIO), and individuals with lived experiences was undertaken to develop a skin check video resource. The process involved engaging a multidisciplinary team and other relevant stakeholders, creating storyboards, filming, and developing iterative feedback loops. Challenges included balancing clinician and user needs. Despite these challenges, an instructional video was successfully developed and integrated into SCI rehabilitation settings across Ontario. The video demonstrates independent skin check and assisted skin check techniques and has been well-received, with 4,400 views to date. Implementation strategies varied across sites, reflecting local contexts and needs. Key findings include the importance of clear communication, stakeholder engagement, and iterative refinement. Future efforts will focus on sustaining and disseminating the video, including translating it into French and further integrating it into staff education.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".