Nursing student community engagement barriers, facilitators and satisfaction: Perceptions of community champions
Bibliographic record
Abstract
Nursing students could benefit professionally by participating in community engagement, but barriers to student involvement in community engagement exist. Community Champions, a nursing student-led, faculty-mentored service-learning group, promotes local outreach and engagement with a variety of community initiatives for nursing students. The purpose of this study was to examine former and current Community Champions perceptions of the barriers and facilitators to participating in community engagement initiatives, as well as their satisfaction with the community engagement initiatives. The study used a 14-item survey, consisting of both Likert-scale items and open-ended questions. Of the 130 Community Champions invited to complete the survey, 40 Community Champions responded (30.8% response rate). Quantitative responses were analyzed using descriptive statistics, and qualitative responses were reviewed for themes to generate future recommendations for program improvement. Respondents rated Community Champions highly and reported personal and professional benefits to community engagement. Reasons for program satisfaction were synthesized into “opportunities for interactions with diverse community members”, “stress relief”, and “professional development”; reasons for program dissatisfaction were summarized as “disorganization of community sites”, “competing academic priorities”, and “lack of information”. Academic student programs that engage the community can positively impact both the community partners and university students.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".