Skin Tear Management: A Multidisciplinary Education Project
Bibliographic record
Abstract
OBJECTIVE: To identify the number of skin tears present at the authors' facility and evaluate a multidisciplinary educational intervention to support treatment of skin tears. METHODS: The authors determined the prevalence of skin tears from an analysis of a wound audit dataset at Baycrest Health Sciences and compared it with the literature to inform the aims of the educational intervention. They developed an educational module and presented it to physicians and students at separate in-person sessions and to clinical care staff at a virtual session. Participants completed an evaluation survey after the education sessions to assess their knowledge and confidence with skin tear management and obtain their feedback about the session. RESULTS: The prevalence of skin tears at Baycrest hospital was 5.6%, which was low compared with the values reported in the literature. For the 10 studies reviewed, the median prevalence was 8.8% (range, 3.0%-22.1%). A total of 7 physicians, 12 students, and 7 clinical care staff completed the evaluation survey. All of the physicians (100%), 43% of students, and 57% of clinical care staff could classify an image of a skin tear; 86% of physicians, 33% of students, and 43% of clinical care staff identified the correct skin tear complications; and 71% of physicians, 0% of the students, and 29% of clinical care staff selected the appropriate dressing. Participants reported moderate to considerable increases in knowledge and confidence in skin tear management. CONCLUSIONS: This method of multidisciplinary teaching on skin tears was well received and useful in enhancing knowledge and confidence in identifying and treating skin tears.
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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 | 0.009 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".