Student Reflections on the Queen’s Accelerated Route to Medical School Programme
Bibliographic record
Abstract
CONTEXT: Since its inception more than 150 years ago, the School of Medicine at Queen's University has aspired 'to advance the tradition of preparing excellent physicians and leaders in health care by embracing a spirit of inquiry and innovation in education and research'. As part of this continuing commitment, Queen's School of Medicine developed the Queen's University Accelerated Route to Medical School (QuARMS). As Canada's only 2-year accelerated-entry premedical programme, QuARMS was designed to reduce training time, the associated expense of medical training, and to encourage a collaborative premedical experience. Students enter QuARMS directly from high school and then spend 2 years enrolled in an undergraduate degree programme. They then are eligible to enter the first-year MD curriculum. The 2-year QuARMS academic curriculum includes traditional undergraduate coursework, small group sessions, and independent activities. The QuARMS curriculum is built on 4 pillars: communication skills, critical thinking, the role of physician (including community service learning [CSL]), and scientific foundations. Self-regulated learning (SRL) is explicitly developed throughout all aspects of the curriculum. Medical educators have defined SRL as the cyclical control of academic and clinical performance through several key processes that include goal-directed behaviour, use of specific strategies to attain goals, and the adaptation and modification to behaviours or strategies that optimize learning and performance. Based on Zimmerman's social cognitive framework, this definition includes relationships among the individual, his or her behaviour, and the environment, with the expectation that individuals will monitor and adjust their behaviours to influence future outcomes. OBJECTIVES: This study evaluated the students' learning as perceived by them at the conclusion of their first 2 academic years. METHODS: At the end of the QuARMS learning stream, the first and second cohorts of students completed a 26-item, 4-point Likert-type instrument with space for optional narrative details for each question. A focus group with each group explored emergent issues. Consent was obtained from 9 out of 10 and 7 out of 8 participants to report the 2015 survey and focus group data, respectively, and from 10 out of 10 and 9 out of 10 participants to report the 2016 survey and focus group data, respectively. Thematic analysis and a constructivist interpretive paradigm were used. A distanced facilitator, standard protocols, and a dual approach assured consistency and trustworthiness of data. RESULTS: Both analyses were congruent. Students described experiences consistent with curricular goals including critical thinking, communication, role of a physician, CSL, and SRL. Needs included additional mentorship, more structure for CSL, more feedback, explicit continuity between in-class sessions, and more clinical experience. Expectations of students towards engaging in independent learning led to some feelings of disconnectedness. CONCLUSIONS: Participants described benefit from the sessions and an experience consistent with the curricular goals, which were intentionally focused on foundational skills. In contrast to the goal of SRL, students described a need for an explicit educational structure. Thus, scaffolding of the curriculum from more structured in year 1 to less structured in year 2 using additional mentorship and feedback is planned for subsequent years. Added clinical exposure may increase relevance but poses challenges for integration with the first-year medical class.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".