Comparison of peer-tutor and librarian feedback for the literature search component of a medical school research course
Bibliographic record
Abstract
Introduction: The aim of this study is to compare the peer tutor and librarian feedback on second year medical students’ literature search skills as part of a research course at Queen’s University, Kingston, Ontario, Canada. Methods: Student peer tutors and medical librarians each assessed a sample of literature searches for a culminating project. Two separate student cohorts were evaluated, and the marked rubrics were compared. Students also participated in focus groups. An online survey was sent to a third cohort of students who did not work with peer tutors, but instead met with librarians one-on-one to discuss their literature searches. Results: There was a measurable difference in the mark agreement between the peer tutors and the librarians. Unsurprisingly, librarians identified important errors and omissions unseen by the peer tutors. Peer tutors found the process of peer assessment very useful for their own learning and teaching skill development, however, the non-peer tutor students did not appreciate the value of this methodology. After peer tutoring was discontinued, the survey feedback was very positive about the value of the individual librarian consultations. Discussion: Medical students conducting a research project need to perform thorough literature searches. Although librarians found the consultations time-consuming, they found that the consultations improved searches more than having students receive help from peer tutors in the same class. The surveyed students were positive about the librarian consultation. Author keywords: Medical students; critical enquiry; student research; Peer tutoring; Assessment; Program evaluation; Librarian consultations; Information literacy; Focus groups, Online survey.
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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 | 0.023 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".