SWEP’d up for the Summer: Survey of participants’ experiences of a nursing student research internship
Bibliographic record
Abstract
Background: Queen’s University (Ontario, Canada) has been offering the Summer Work Experience Program (SWEP) - a subsidized work opportunity for university undergraduate students - since 1995. The Queen’s University Nursing and Health Research Internship program, established in May 2017, involves nursing SWEP students. The program was designed to build research knowledge and experience for nursing students. The aim of this article is to describe the program components, and intern and faculty insights.Methods: To obtain feedback from current and past interns and faculty, an electronic survey was distributed. Data were analyzed for common themes.Results: Themes consolidated from interns (n = 4) included challenges as learning opportunities, new perspectives on research, and successes and opportunities. Themes that emerged from faculty (n = 7) were program challenges and successes, and needs and concerns of interns.Conclusions: Overall, interns and faculty members perceived the program as a valuable learning experience. Suggestions for program development and potential changes are discussed from both an intern and faculty perspective. Further recommendations for program development are explored, with potential changes for future offerings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".