Prevention and treatment of pressure ulcers in a university hospital centre: A correlational study examining nurses' knowledge and best practice
Bibliographic record
Abstract
Claudia G, Diane M, Daphney SG, Danièle D. International Journal of Nursing Practice 2010; 16: 183–187 Prevention and treatment of pressure ulcers in a university hospital centre: A correlational study examining nurses' knowledge and best practice This descriptive correlational study had the goal of exploring if relationships existed between the level of knowledge of nurses concerning pressure ulcers, certain nurses' characteristics and the preventive care they applied. A multi‐method approach was taken using a questionnaire to measure the level of knowledge of nurses (n = 256) and chart audits (n = 235) to identify the preventive care applied. The results show that the level of knowledge of the nurses is insufficient. They also show a correlation between a higher level of knowledge and (i) the sector of activities in which the nurses are working, (ii) the training periods provided by the university hospital centre, and a (iii) good perception by the nurses of their level of knowledge. However, training on its own cannot guarantee the provision of quality health care, as there is a wide discrepancy between what nurses know and what they put into practice.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".