LO11: STAR-EM: An innovative summer research program for medical students in an urban Canadian academic emergency department
Bibliographic record
Abstract
Innovation Concept: Research training programs for students, especially in emergency medicine (EM), may be difficult to initiate due to lack of protected time, resources, and mentors (Chang Y, Ramnanan CJ. Academic Medicine 2015). We developed a ten-week summer program for medical students aimed at cultivating research skills through mentorship, clinical enrichment, and immersion in EM research culture through shadowing and project support. Methods: Five second year Ontario medical students were recruited to participate in the Summer Training and Research in Emergency Medicine (STAR-EM) program at University Health Network, Toronto, from June - Aug, 2019. Program design followed review of existing summer research programs and literature regarding challenges to EM research (McRae, Perry, Brehaut et al. CJEM 2018). The program had broad emergency physician (EP) engagement, with five EP research project mentors, and over ten EPs delivering academic sessions. Curriculum development was collaborative and iterative. All projects were approved by the hospital Research Ethics Board (REB). Curriculum, Tool or Material: Each weekly academic morning comprised small group teaching (topics including research methodology, manuscript preparation, health equity, quality improvement, and wellness), followed by EP-led group progress review of each student's project. Each student spent one half day per week in the emergency department (ED), shadowing an EP and identifying patients for recruitment for ongoing mentor-initiated ED research projects. Remaining time was spent on independent student project work. Presentation to faculty and program evaluation occurred in week 10. Scholarly output included one abstract submitted for publication per student. Program evaluation by students reflected a uniform impression that course material and mentorship were each excellent (100%, n = 5). Interest in pursuing academic EM as a career was identified by all students. Faculty researchers rated the program as very effective (80%, n = 4) or somewhat effective (20%, n = 1) in terms of enhancing productivity and scholarly output. Conclusion: The STAR-EM program provides a transferable model for other academic departments seeking to foster the development of future clinician investigators and enhance ED research culture. Program challenges included delays in REB approval for student projects and engaging recalcitrant staff to participate in research.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".