Exploring the “led” in health professional student-led experiences: a scoping review
Bibliographic record
Abstract
To support a complex health system, students are expected to be competent leaders as well as competent clinicians. Intentional student leadership development is needed in health professional education programs. Student-led experiences such as student-run clinics and interprofessional training wards, are practice-based learning opportunities where learners provide leadership to clinical services and/or address a gap in the system. Given the absence of leadership definitions and concepts, this scoping review explored how student leadership is conceptualized and developed in student-led experiences. The review was conducted in accordance with best practices in scoping review methodology within the scope of relevant practice-based student-led experiences for health professional students. The research team screened 4659 abstracts, identified 315 articles for full-text review and selected 75 articles for data extraction and analysis. A thematic analysis produced themes related to leadership concepts/theories/models, objectives, facilitation/supervision, assessment and evaluation of curriculum. While responding to system gaps within health professional care, student-led experiences need to align explicit leadership theory/concepts/models with curricular objectives, pedagogy, and assessments to support health professional education. To support future student-led experiences, authors mapped five leadership student role profiles that were associated with student-led models and could be constructively aligned with theory and concepts. In addition to leveraging a student workforce to address system needs, student-led experiences must also be a force for learning through a reciprocal model of leadership and service to develop future health professionals and leaders.
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 | 0.034 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".