Understanding Student-Run Health Initiatives in the Context of Community-Based Services: A Concept Analysis and Proposed Definitions
Bibliographic record
Abstract
BACKGROUND: Student-run health initiatives in the community setting have been utilized to provide practical experience for undergraduate students to develop professional competencies, gain exposure to diverse populations, and to engage in activities of social accountability. There is much literature on student-run health initiatives; however, there is no consensus on a definition of this concept or a comprehensive synthesis of the literature that describes student-run health initiatives offered by students in pre-licensure healthcare education programs. PURPOSE: To provide a concept analysis of, and propose a definition for, student-run health initiatives that provide community-based services for students during pre-licensure health discipline education. METHODS: A systematic literature search and review process was used to identify and synthesize peer-reviewed articles from 7 academic databases covering a range of pre-licensure health disciplines and education. Walker and Avant's framework for concept analysis was used to guide exploration of attributes, antecedents and consequences of student-run initiatives, and to inform development of a definition for this concept. RESULTS: The review yielded 222 articles for data extraction and represented 17 distinct pre-licensure health disciplines, 18 health-related disciplines, and a range of other baccalaureate and graduate programs. Our analysis revealed 16 definitions, 5 attributes, 6 antecedents, and consequences identified for student-run health initiatives. Attributes were Provision of Service, Service is Free, Target Clientele, Volunteerism, and Student Governance. Antecedents included Purpose/Rationale, Affiliation with Academic Unit, Location and Partnerships, Funding and Resources, Professional Oversight, and Preparation for Student Role. Consequences were improved access to services and outcomes for clients; competency development, personal gains and interprofessional learning for students; and positive outcomes for broader systems, such as decrease of service utilization and cost/benefit. CONCLUSIONS: There was no clear conceptual definition for student-run health initiatives, but many defining characteristics and well-described exemplars in the literature. Given the variations in purpose and scope of these initiatives, particularly to distinguish degree of students' roles in operations and the involvement of academic institutions, we propose 3 distinct conceptual definitions: student-run, student-led, and student-infused health initiatives.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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".