Evaluating effectiveness of small group information literacy instruction for<scp>U</scp>ndergraduate<scp>M</scp>edical<scp>E</scp>ducation students using a pre‐ and post‐survey study design
Bibliographic record
Abstract
BACKGROUND: The Undergraduate Medical Education (UME) programme at the University of Calgary is a three-year programme with a strong emphasis on small group learning. OBJECTIVE: The purpose of our study was to determine whether librarian led small group information literacy instruction, closely integrated with course content and faculty participation, but without a hands on component, was an effective means to convey EBM literacy skills. METHOD: Five 15-minute EBM information literacy sessions were delivered by three librarians to 12 practicing physician led small groups of 15 students. Students were asked to complete an online survey before and after the sessions. Data analysis was performed through simple descriptive statistics. RESULTS: A total of 144 of 160 students responded to the pre-survey, and 112 students answered the post-survey. Instruction in a small group environment without a mandatory hands on component had a positive impact on student's evidence-based information literacy skills. Students were more likely to consult a librarian and had increased confidence in their abilities to search and find relevant information. CONCLUSION: Our study demonstrates that student engagement and faculty involvement are effective tools for delivering information literacy skills when working with students in a small group setting outside of a computer classroom.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".