Nurses' knowledge and related factors towards hemodialysis patients' nutrition: A systematic review
Bibliographic record
Abstract
This systematic review was conducted with the objective of assessing the nurses' knowledge and related factors towards hemodialysis patients' nutrition. A thorough and systematic search was executed across various international electronic databases, including Scopus, PubMed, and Web of Science, as well as Persian electronic databases such as Iranmedex and the Scientific Information Database. The search utilized keywords derived from Medical Subject Headings, including “knowledge”, “nurses”, “hemodialysis”, and “nutrition”, and covered all available literature up to March 11, 2024. The quality of the studies incorporated into this systematic review was assessed using the Appraisal tool for Cross-Sectional Studies (AXIS tool). The review encompassed six cross-sectional studies, involving a total of 455 hemodialysis nurses. Among the participants, 57.47% were female. The geographical distribution of the studies included in this review was as follows: four studies were conducted in Iraq, one in Italy and one in Greece. The mean nutritional knowledge score among hemodialysis nurses, as reported in six studies, was 57.40 out of 100. This score suggests a moderate level of nutritional knowledge among the nurses. Several factors, such as age, level of education, and years of experience, were identified as being associated with the nutritional knowledge of hemodialysis nurses. Therefore, it is recommended that nursing policymakers and managers focus on these factors to enhance the nutritional knowledge among hemodialysis nurses. This could potentially lead to improved patient outcomes in the hemodialysis setting.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".