Building a foundation for utilizing evidence based practice in conjunction with research in baccalaureate nursing program
Bibliographic record
Abstract
Less is known about the undergraduate nursing students’ ability to conduct beginning research. This study aims to explore and describe nursing students' experiences in planning and implementation of community health fairs; and utilizing evidence based practice integrated with research as a learning outcome. The study using a quantitative and descriptive design was conducted by senior nursing students during diverse community health events as a part of Gerontological nursing clinical. The students utilized three fall assessment tools: Balance and Gait test, Timed Up and Go test, and 10-year Fracture Risk Calculation. A total of 74 students participated in seven community health fair events focused on Fall Prevention. This health fair event was in conjunction with the National Fall Prevention Awareness week. A total of 201 older adults were served during the event by nursing students who provided screening process and related health education. Data were gathered and a group of students volunteered to complete the research process. The students participated in oral presentation in the Annual University Research Student Symposium and had poster presentation in the professional academic conference. Early systematic organized planning of the clinical experience gives students opportunity to integrate evidence based practice into research. Application of varied evidence based assessment tools focused on older adults enables students to understand the health issue in depth and the need for additional services. Health fair experiences improve students’ communication and education skills, reality of health issues of the target population in a community, and evidence based research.
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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 | 0.459 | 0.428 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".