Medical Student Utilization of a Novel Web-Based Platform (Psy-Q) for Question-Based Learning in Psychiatry: Pilot Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Medical students are turning to new and expanding web-based resources for learning during their psychiatry clerkships; however, there have not been concomitant efforts by educators to utilize web-based tools to promote innovative teaching. OBJECTIVE: Utilizing a free learning platform (Psy-Q) created by our team, we sought to explore how digital technology may engage medical student learners, promote colearning between educators and medical students, and support sustainability of web-based platforms through crowdsourcing. METHODS: Between 2017 and 2019, seven medical schools offered access to the platform during medical students' psychiatry clerkships. Use of the web-based platform was voluntary and not monitored or related to clerkship evaluation. Medical students completed a paper and pencil assessment of the platform at the end of their clerkship. Anonymous and aggregated website use data were gathered in accordance with institutional review board approval. RESULTS: A total of 203 medical students across seven distinct psychiatry clerkships completed the survey. Of these students, 123 (60.6%) reported using the platform and reported accessing a mean of 45 questions. The most common device used to access the platform was a laptop and the second most common was a smartphone. The most common location to access the platform was home and the second most common was the hospital. Although few students contributed new questions, website utilization data suggested that all rated the quality and difficulty of the questions. Higher quality questions were medical students' main suggestion for further improvement. CONCLUSIONS: Our results suggest the feasibility and potential of educator- and learner-created web-based platforms to augment psychiatry education and develop relevant accessible resources in the digital sphere. Future work should focus on measuring objective educational outcomes of question taking and writing, as well as optimizing technology and exploring sustainable trainee-faculty partnership models for the creation and curation of content.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".