Graduate student service learning in medically underserved communities
Bibliographic record
Abstract
Introduction : This qualitative analysis aimed to ascertain the impact of community-oriented service learning experiences on community engagement of nurse practitioner students through the analysis of written student experience reflections. The UCSF Elev8 Healthy Students & Families project marked the beginning of an ongoing interprofessional academic-practice partnership in which health science graduate students have been assigned to service learning projects in school based health centers located in medically underserved neighborhoods. Methods : Semi-structured self-reflections were collected from nurse practitioner and dental students between 2011 and 2015. Sixty graduate students provided written reflections before, during and after their service learning experiences. Dimensional analysis, a form of grounded theory, was employed as the primary analytic strategy. Results : Several major processes were identified, including interprofessional learning and communication development. Tangible experiences with the social determinants of health proved centrally important to effective learning. Important conditions impacting the student experience were whether or not students were from or had experience in underserved communities and how they perceived the orientation/preparation. Conclusions : This project provided valuable opportunities for growth as clinicians, including familiarization with community engagement, communication skills, interprofessional opportunities, and role modeling possible career pathways for community youth. Academic institutions partnering with community health sites for service learning should integrate thoughtful orientation to sites and community health topics. Finally, creating a space to discuss how a student’s own personal background impacts their experiences is critical and may serve to enrich the opportunity for all students involved.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".