Application of an innovative, autonomous, creative teaching modality through service-learning in a community-health nursing course
Bibliographic record
Abstract
Nursing faculty continue to find themselves challenged to meet student needs by the increasing student enrollment numbers and increasing faculty workloads without simultaneously increasing resources. The responsibility to meet student needs rests on the nursing faculty. It is therefore increasingly important that nursing faculty implement teaching modalities to meet student and patient needs. This article demonstrates faculty’s use of service-learning as an effective, innovative teaching modality to meet increasing student, patient, and community needs without additional resources. The authors seek to differentiate between service learning and contracted clinical experiences in order to enable nurse educators to use service learning as a teaching modality. The authors describe the process of using service learning with nursing process in this course. Nursing assessment is built into the project as a “windshield survey”. A literature review was conducted seeking to understand other uses of service learning in education and validate the authors’ experiences. This three-credit hour lecture and one credit hour clinical course has yielded unique and interesting service learning projects that positively impact their communities. The students work with cohorts of all races, ethnicities and cultures throughout the lifespan. Service-learning opportunities assist in the availability and accessibility of health care to safety net facilities and vulnerable populations who may not otherwise receive health care screening or treatment. Faculty in nursing and other health disciplines will likely appreciate the innovation, creativity, and autonomy afforded this modality of outreach.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".