Impact of librarians in first‐year medical and dental student problem‐based learning (PBL) groups: a controlled study
Bibliographic record
Abstract
BACKGROUND: Librarians at the University of Alberta have been involved with teaching undergraduate medical and dental education for several years. After 1 year of increased librarian involvement at the problem-based learning (PBL), small-group level, informal feedback from faculty and students suggested that librarians' participation in PBL groups was beneficial. There was, however, no real evidence to support this claim or justify the high demand on librarians' time. OBJECTIVES: The study aimed to determine whether having a librarian present in the small-group, problem-based learning modules for first-year medical and dental students results in an improved understanding of evidence-based medicine concepts, the nature of medical literature, and information access skills. METHODS: One hundred and sixty-four first-year medical and dental students participated in the study. There were a total of 18 PBL groups, each with approximately nine students and one faculty tutor. Six librarians participated and were assigned randomly to the six intervention groups. Students were given pre- and post-tests at the outset and upon completion of the 6-week course. RESULTS: Post-test scores showed that there was a small positive librarian impact, but final exam scores showed no impact. There was also no difference in attitudes or comfort levels between students who had a librarian in their group and those who did not. CONCLUSIONS: Impact was not sufficient to warrant continued participation of librarians in PBL. In future instruction, librarians at the John W. Scott Health Sciences Library will continue to teach at the larger group level.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".