A survey of nurses' experience integrating oncology clinical and academic worlds
Bibliographic record
Abstract
AIM: To better understand how oncology nurses (a) navigate graduate studies; (b) perceive the impact of their academic work on their clinical practice, and vice versa; and (c) engage with clinical settings following graduate work. DESIGN: Interpretive descriptive cross-sectional survey. METHODS: A qualitative exploratory web-based survey exploring integration of graduate studies and clinical nursing practice. RESULTS: About 87 participants from seven countries responded. 71% were employed in clinical settings, 53% were enrolled in/graduated from Master's programs; 47% were enrolled in/graduated from doctoral programs. Participants had diverse motivations for pursuing graduate studies and improving clinical care. Participants reported graduate preparation increased their ability to provide quality care and conduct research. Lack of time and institutional structures were challenges to integrating clinical work and academic pursuits. CONCLUSIONS: Given the many constraints and numerous benefits of nurses engaging in graduate work, structures and strategies to support hybrid roles should be explored.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".