Learning module for BSCN students at the University of Ontario Institute of Technology for the risk management of stage I pressure ulcers
Bibliographic record
Abstract
Background: Student nurses need to develop competency providing nursing care to \npatients at risk of developing a Stage I Pressure Ulcer in the clinical setting. This \npracticum developed an educational tool for The Bachelor of Science in Nursing students \nat The University of Ontario Institute of Technology for the risk management of Stage I \nPressure Ulcers. \nObjectives: The objectives of this clinical project are: 1) To conduct a thorough literature \nreview to critically appraise evidence-based information and clinical practice guidelines \nrelated to the risk management of Stage I Pressure Ulcers, 2) To conduct consultations \nwith key stakeholders to determine relevant clinical skills and techniques used to \nimplement risk management of Stage I Pressure Ulcers, and 3) To develop the \nappropriate educational resource tool based on learning needs identified during \nconsultations for the risk management of Stage I Pressure Ulcers. \nMethodology: A literature review and consultations were used to determine the learning \nneeds of the students within the program to determine relevant information to be included \nin the educational tool. The educational resource was used Knowles Adult Learning \nTheory as the theoretical framework underpinning the development of the project. \nResults: Findings demonstrated that the students experience difficulty with the ability to \nassess, interpret, and develop care plans based on The Braden Scale scores of their \nclinical assignments
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.318 | 0.086 |
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".