An undergraduate collaborative team model to engage nursing students in research
Bibliographic record
Abstract
Purpose: Student-faculty collaboration on research is an effective model to engage undergraduate students in the research process outside the traditional curriculum. Using this model, a student-faculty team developed a longitudinal study about college students’ alcohol use behaviors and implemented an intervention to influence the culture of drinking on campus.Methods: A longitudinal design was used to assess drinking behaviors and evaluate the effect of a mass media campaign with social norm messages and alcohol education. Undergraduate students on a faith-based, Midwestern campus completed baseline (N = 1,095) and post-intervention (N = 1,011) electronic surveys.Results: In addition to helping students learn about and develop enthusiasm for research, this project had an impact on the drinking culture on campus. Findings showed 88% of students observed media campaign messages with 82% viewing the printed posters, 47% viewing the outdoor displays, and 25% viewing messages on social media. There was a significant change in binge drinking from the pre- (72%) to post-intervention (40%) surveys.Conclusions: Collaborative undergraduate research teams are an effective model to help students learn how to carry out research and develop interest and enthusiasm for the process. The outcomes of the project demonstrated interventions were effective at influencing the drinking culture on campus. The development of a research program outside the required curriculum can be a successful strategy to engage students in all phases of the research process, increase enthusiasm for research, and enhance health care outcomes in various settings.
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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 | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".