Knowledge, Attitude and Practice of Nurses Regarding Organ Donation
Bibliographic record
Abstract
INTRODUCTION: Treatment team charged to help patients and their family making decision about donate organs in the final stage of life. Hence, their knowledge and attitude is important to plan of increasing the rate of organ donation. MATERIALS AND METHODS: About 150 nurses recruited in this cross-sectional study randomly. After taking informed consent, questionnaires were filled. The data collection tool was a multipart questionnaire including demographic information, 18 questions about attitude and practice and 15 question about knowledge toward organ donation. Data were analyzed by SPSS software using K-squire, Pearson correlation test, T-test, variance analyze on 95% confidence interval. RESULTS: Most of participants (76%) were 25-44 years old. About 81.3% of them were female (n=122). The attitude average score between males and females was 85.25±35.61 and 70.37±46.53, respectively. The practice average score in females was 34.43±47.71 and between males was 29.63±46.53. The knowledge average scores were 50.60±16.19 and 56.54±17.48 for two groups (p>0.05). The knowledge average scores between different age groups was significant (p<0.05). There was a direct and significant relation between attitude and practice (r=+0.33, p<0.05), attitude and Factors influencing attitude and practice (r=0.866, p<0.05), but the relation between attitude and knowledge was indirect and significant (r=-0.183, p<0.05). CONCLUSIONS: Since the medical team are most important adviser for promote activities related to organ donation, it seems that educational curriculum and facilities should applied to enhance attitude and behavior favorable change of personnel towards this issue.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".