Prevalence of Skin Tears in a Long-term Care Facility
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to collect baseline data of the prevalence of skin tears in a Canadian long-term care (LTC) facility. SUBJECTS AND SETTING: The research setting was a 114-bed long-term care facility located in Eastern Ontario, Canada. The sample population comprised 113 residents from the facility. DESIGN: A cross-sectional, quantitative study design was used to gather baseline data on the prevalence of skin tears in the Canadian population living in LTC. METHODS: Residents were assessed for presence of skin tears, the number of skin tears, and location. Skin tears were categorized according to the validated Payne Martin Classification system. Data were collected using a predetermined data collection sheet developed for this study. A certified enterostomal therapy nurse with previous experience with the assessment of skin tears collected the data along with 1 nurse employed by the facility. Data were collected on a single day over a 6-hour period. RESULTS: Twenty-five of the 113 participating residents in the LTC facility had skin tears, yielding a prevalence of 22%. Category I accounted for 51% of skin tears, 16% were category II, and 33% were category III. Individuals who were found to have more than 1 skin tear had at least 1 category III skin tear. The most common anatomical locations were arms (48%), lower legs (40%), and hands (12%). Possible etiologic factors included blunt trauma such as banging into objects (44%), trauma associated with activities of daily living (20%), and falls (12%); 24% were categorized as idiopathic. CONCLUSION: Study findings highlight gaps in our knowledge of skin tears and the need for additional studies to more clearly define their epidemiology.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".