Bibliographic record
Abstract
The purpose of the study was to determine research priorities among Norwegian nurses in cancer care, and to investigate implications that these priorities might have for future planning of nursing research. Differences between specialists in cancer nursing and other nurses working in cancer care, and between the current results and earlier findings in this area also were evaluated. Half the members of The Norwegian Society of Nurses in Cancer Care (n = 197) were mailed a questionnaire used in a similar Canadian study. The nurses were asked to select the five topics they perceived as most important from a list of 80 items, and to rank them in order of research priority. The response rate was 43% (197/464), and 75 respondents were specialists in cancer nursing. Quality of life was given the highest research priority in the total sample. Psychosocial support/counseling, communication between patient and nurse, patient participation in decision making, nurse burnout, and ethics also were ranked highly. In contrast to the others, cancer nursing specialists ranked ethics as their number one priority. Except for symptom management, the priorities given in Norway and other Western countries were found to be similar. These results might suggest topics for future research tailored to the needs of cancer nursing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.226 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".