Community champions: A mixed methods study on volunteer recruitment and retention in community engagement
Bibliographic record
Abstract
Community engagement is an effective method of preparing nursing students to be influential providers for diverse patient populations. Over the course of the 2016-2017 academic year, volunteer attendance was recorded and a qualitative survey was distributed to evaluate attendance rates and retention of Community Champion volunteers, and to determine factors that contributed to the success and sustainability of the program. There was an 83% attendance rate overall at the community-based initiatives, with the highest attendance rate of 98% amongst initiative leaders. The following themes emerged from the qualitative surveys assessing retention: 1) Self motivation and enthusiasm among community members 2) diverse and interdisciplinary interactions 3) communication and organization and 4) student commitment barriers. Students with the greatest amount of experience with community engagement assumed more responsibility and dedicated the most amount of time to the program. The consistent commitment of volunteers to Community Champions has positively impacted the students’ academic careers and the sustainability of the community partnership. In order to optimize community programming, volunteer reliability, consistency and commitment are necessary.
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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 | 0.037 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".