The perceptions of community-based organizations collaborating with nursing faculty to promote students’ public health nursing competencies
Bibliographic record
Abstract
Collaborative community-based organizations (CBOs) and academic partnerships are a prerequisite for the creation of quality learning environments for undergraduate nursing students. However, the explicit nature of the relationship between academic and CBO partners is not as well-defined as the one between hospitals and their clinical settings. The aim of this study was to describe and evaluate the implementation and impact of a 3-year-long partnership between a nursing school and 20 different CBOs. Semi-structured individual interviews were conducted with 11 CBO partners throughout June and July of 2018. Interview questions explored the collaborative process, its benefits, and areas for improvement. Study participants reported that the partnerships brought several benefits, including familiarizing students with marginalized populations, demystifying the health care system for the populations served by the CBOs, and the students’ development of sustainable health promotion tools that contributed positively to the CBOs’ overall mission. Challenges identified by the CBOs included finding resources to provide adequate student supervision and population access, and some students’ challenges with adapting to the CBOs’ client population or community environment. Collaborative partnerships were mutually beneficial for populations, students and the community organizations. These results support the establishment and long-term development of these types of partnerships.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".