What areas of cancer care do Norwegian nurses experience as problems?
Bibliographic record
Abstract
BACKGROUND: As the number of people diagnosed with cancer increases and the treatment of cancer changes and improves, the challenges and problems nurses experience in clinical practice are also changing. AIMS: To describe the problems that nurses in cancer care face in their day-to-day practice. Such information can be used to improve the quality of nursing care. DESIGN: A questionnaire was distributed to half of the members of the Norwegian Society of Nurses in Cancer Care (n = 464). Instrument. The survey questionnaire had previously been used in a Canadian study. The nurses were asked to consider 80 different areas relevant to cancer nursing and to indicate the extent to which each posed a problem for them in clinical practice. The sex, age and educational level of the nurses were also recorded, together with the work setting and number of years they had been employed in nursing. RESULTS: The response rate was 43% (199 of 464). The area the nurses experienced as being the greatest problem in their clinical practice was patients' anxiety. Problems connected with nutrition, the development of the cancer itself and grieving, and symptoms like nausea and vomiting and fatigue were also frequently rated as problems. The results also show some significant correlations between the rating of problems and variables such as work setting, years in practice and educational level. CONCLUSIONS: The present study shows that nurses in cancer care mainly experience psychological issues as problems in their clinical practice, which confirms previous research. The impact of work setting, years in clinical practice and education was surprisingly small. As the majority of nurses today will meet cancer patients during their professional careers, further research is needed in this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".