Lifestyle Counseling Practices of Oncology Nurses in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: Oncology nurses are well poised to provide lifestyle behavior counseling to cancer survivors. However, very little is known about the current lifestyle behavior counseling practices of oncology nurses. OBJECTIVES: The primary purpose of this study was to examine lifestyle behavior counseling practices of oncology nurses. The secondary purpose was to examine differences in lifestyle behavior counseling based on nurses' health behaviors, additional training received, and country of residence. METHODS: Oncology nurses (N = 314) were primarily recruited through emails from oncology nursing email lists. Participants completed an online survey. FINDINGS: Overall, oncology nurses reported providing lifestyle counseling to most cancer survivors when appropriate. The majority of oncology nurses said they were receptive to receiving additional training about lifestyle counseling. Participants who had received additional training and who lived in the United States were more likely to provide counseling to cancer survivors. Concerning their own health behaviors, no meaningful differences were found regarding lifestyle behavior counseling practices, which contrasts with findings from previous research. Strengthening oncology nurses' lifestyle behavior counseling knowledge and skills may offer opportunities to enhance survivors' optimal health and quality of life and to reduce their risk of recurrence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".