Workshop (Clinical/Best Practice Implementation) ID 2001167
Bibliographic record
Abstract
Background Hamilton’s Spinal Cord Injury (SCI) Regional Rehabilitation Program in collaboration with SCI Consortium is implementing tissue integrity monitoring for pressure injury (PI) prevention. An increased prevalence of PI was found in 2021 - 2022 compared to 2020 on Hamilton’s SCI Rehabilitation Unit. To address this finding, the clinical team introduced a staff and patient education program focused on PI prevention. Upon completion of this workshop, the attendees will be aware of the CQI methodology used to implement the best practices related to tissue integrity and appreciate the education pathway for both staff and patients. Methods Using a Continuous Quality Improvement model, a systematic approach was used to enhance the education model for staff and patients on skin assessment and appropriate interventions. A process map was created outlining steps for each discipline involved in skin assessment, intervention, and patient education. Education plan included Skin & Wound Management workshop, individual and group patient education, daily skin check calendar for patient/staff use. The workshop will be delivered in a lecture format, ending with discussion time. Results Preliminary data post partial implementation of the education program showed a decrease of 12% in the prevalence of PIs in the patients discharged from the program between July 2022 and March 2023 compared to January 2021 – June 2022. Conclusion Preliminary results are encouraging in demonstrating that implementation of a comprehensive education plan for staff and patients using a structured framework is effective in reducing the prevalence of pressure injuries in the inpatient SCI rehab unit.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.370 | 0.157 |
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".