Integrating open science education into an undergraduate health professional research program
Bibliographic record
Abstract
Objective: Open science (OS) is a global movement focused on improving research equity, reproducibility, and transparency of research outputs in publicly funded research. While OS education in academia is becoming more common, examples of health sciences librarians providing OS training are not. This paper describes how a librarian collaborated with teaching faculty and a research program coordinator to integrate an OS curriculum into an undergraduate professional practice course and assess students' perceptions of OS after participating. Methods: A librarian developed an OS-specific curriculum for an undergraduate professional practice course in Nutrition. This course is part of the First Year Research Experience (FYRE) program, which is integrated into 13-week undergraduate courses to introduce students to core elements of the research process in their first year of study by carrying out a research project. The OS curriculum included an Introduction to OS class, a requirement that students share their research outputs in the Open Science Framework, and an assignment asking students to reflect on their experience learning about and practicing OS. Twenty-one of 30 students consented to having their reflection assignment undergo thematic analysis. Results: Students indicated transparency, accountability, accessibility to research outputs, and increased efficiency as positive attributes of OS. The time commitment, fear of being scooped, and concerns over having research be misinterpreted were considered negative attributes. 90% (n=19) of students indicated that they intend to practice OS in the future. Conclusion: Based on strong engagement from the students, we believe that this OS curriculum could be adapted to other undergraduate or graduate student contexts where a research project is required.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".