Faculty survey of service-learning and its impact on nursing students
Bibliographic record
Abstract
Objective: Service-learning experiences (SLEs) help instill cultural competence and prepare nurses for practice with diverse populations in varied settings. This study describes SLE activities implemented by supervising nursing faculty, explores faculty opinions about the importance of those activities to uncover disparities between practice and values, and solicits faculty opinions about the impact of service learning on the students.Methods: An online, quantitative survey collected data from faculty who lead SLEs in US nursing programs. Results: A total of 77 US nursing faculty from 32 states reported on SLEs, 23% of which were located outside of the US. Pre-experiential, experiential, and post-experiential SLE discussion topics most often included the host community healthcare system, health and economic disparities, cultural norms, and benefits of the SLE. Religious beliefs, poverty tourism, racism, and privilege were discussed less often. Students participated in a variety of nursing-related activities onsite and nearly all faculty required follow up activities. Most faculty agreed that meeting the immediate needs of the host community, building sustainable partnerships with host community, addressing personal growth of the students, and discussing inequities are important aspects of an SLE, although actual implementation of those activities varied. Students feel “changed” after the SLE and become more likely to advocate for the vulnerable and underserved, but can also feel overwhelmed and harbor guilt about inequities.Conclusions: Faculty report a wide range of discussion-based and hands-on activities in the pre-experiential, experiential, and post-experiential phases of the SLE. Overall, faculty believe that service learning positively impacts student development, but feelings of guilt and being overwhelmed can also persist after students return home.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".