Medical student perceptions of research training on patient care during clerkship
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Background Health Science Research (HSR) is a pre-clerkship component of the University of Toronto (U of T) MD Program. Through online modules and tutorials, students learn to understand and apply research, and write an original research protocol. This study explored students' perceptions on how HSR prepared them to identify, critically appraise and consume research during clerkship. Methods An online 12-item questionnaire surveyed U of T medical students (Class of 2018) who completed HSR in 2016. Basic descriptive statistics were performed; free text responses were analysed via descriptive thematic analysis. Results Twenty six percent (67/262) of students participated. Approximately half either agreed/strongly agreed that HSR helped them to critically appraise research articles (50.7%, 32/63) and assess applicability of results to patient care (50.8%, 32/63). Three themes emerged: i) desire for increased critical appraisal, ii) producing research less important than consuming research, iii) developing a greater appreciation of research during clerkship. Conclusions Students' perceptions on HSR's value during clerkship were modest; they desired greater focus on learning to be consumers of research. These results will refine HS, and our observations may be useful to other educators, as this type of intervention is not represented in existing literature.
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 | 0.022 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".