Iranian nurses' educational needs and competence in palliative cancer care
Bibliographic record
Abstract
AIM: This study was conducted to determine the competence and educational needs of Iranian nurses in the field of palliative cancer care. METHOD: This cross-sectional study was performed on nurses working in oncology departments of hospitals in Kerman, in southeast Iran. The data were collected using nurses' core competence in palliative care inventory and a questionnaire for assessing the educational needs of nurses in the field of palliative cancer care. Pearson correlation coefficient, Independent t-test, ANOVA and Linear regression were used to examine the data. RESULTS: 210 nurses participated in this study and the response rate was 98.13%. The results showed that the mean score of educational needs in palliative cancer care was 3.6±0.7. The highest average score was observed in the mental and psychological (3.83±0.89) dimensions, and the lowest in the social dimension (3.34±0.84). The mean score of nurses' competence in palliative cancer care was 1.78±0.51. The highest mean score was related to interpersonal skills (2.28±0.74), and the lowest mean score belonged to the use of Edmonton symptoms evaluation (1.10±1.27). There was a low significant and inverse correlation between nurses' competence and their educational needs. (P<0.001, r=- 0.242). CONCLUSION: This study showed that Iranian nurses have the need for palliative cancer care training. Therefore, it is necessary to assign a higher priority to the evaluation of the clinical competence and educational needs of nurses in different healthcare centres.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".