From Doubt to Drive: Transforming Student Attitudes Toward Research
Bibliographic record
Abstract
Engaging undergraduate students in nursing research is of high significance for capacity building and advancement of the nursing profession especially with current global constraints to health research. Helping students understand the significance of research can position students towards success in leveraging research in their future careers. Currently, while research as a core nursing course is offered in some schools of nursing, it provides an introductory understanding of research methods and does not often contain a practical application component of what students are learning from a theoretical perspective. This editorial provides strategies on how nursing schools, universities, practice-site organizations, and external funding bodies can modify their existing practices to offer direct, application, research-based opportunities for undergraduate nursing students. Particularly, thinking about how assignments can be modified to instruct students about diverse types of publications and knowledge dissemination options can contribute to students feeling like their voice matters and this work has impact beyond a singular course. Offering students opportunities at the university level to receive research mentorship and learn about the conduct of research from inception to dissemination can equip students with the skills they need to lead research upon graduation on practice-related, first-hand issues they are witnessing as nurses. Research shadowing opportunities or involvement in research within organizations where students are practicing can demonstrate the connection between theory and real-world use of research and impact. Finally, advocating for increasing funding opportunities for undergraduate students from external funders can enhance the accessibility and quality of mentorship in research for such students.
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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 | 0.122 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.032 |
| Scholarly communication | 0.030 | 0.015 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".