Embracing uncertainty: medical student perceptions of a pediatric bootcamp developed in response to mandated changes during the pandemic
Bibliographic record
Abstract
BACKGROUND: The start of the COVID-19 pandemic led to both shortened clinical rotations and consequent loss of embedded formal teaching time. In response to these learning gaps, a novel, virtual pediatric bootcamp was developed to provide a consolidated 3-week learning opportunity for clinical medical students. Pre-clinical students were encouraged but not required to participate, given the suspension of clinical patient experiences for all undergraduate medical learners and the uncertainty of when clinical rotations would resume. This group of students were particularly challenged with adapting their learning in response to the pandemic while also preparing to apply their pre-clinical knowledge to solve clinical problems. METHODS: A qualitative thematic analysis was used for this study. Ten semi-structured phone interviews were conducted with second-year medical students to explore their experiences and perceptions of the pediatric bootcamp. The six phases of thematic analysis proposed by Braun and Clark guided data analysis. To ensure rigour, the three aspects of rigour-credibility, transferability and confirmability were utilized throughout the project. RESULTS: Qualitative exploration from semi-structured phone interviews of second-year medical students' perceptions and experiences of this new and unanticipated learning experience revealed four main themes: (a) clinical relevance, describing how students were pushed to think about clinical problems in a new way; (b) timing, which explored conflicts related to competing interests, mental preparedness, and the interval between learning and application; (c) teaching strategies, describing how active learning and interaction were facilitated and challenges that arose; and (d) learning resources, highlighting the curated and accessible resources made available to the students, as well as those resources that learners develop for themselves. CONCLUSIONS: A novel three-week online case-based pediatric bootcamp fostered application of knowledge for clinical reasoning at a time when students were transitioning from preclinical to clinical learning. Students were stretched to balance competing priorities, and the bootcamp curated synchronous and asynchronous learner opportunities while allowing them to reflect on their own learning styles and effective virtual learning strategies. While bootcamps are often used to prepare learners for transitions between clinical stages, our findings suggest the bootcamp format can also facilitate transition from preclinical to clinical roles.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".