First Year Medical Students Use Library Resources Emphasized During Instruction Sessions
Bibliographic record
Abstract
Objective – To determine if library instruction has an effect on resources cited in student reports. Design – Citation analysis. Setting – The study took place in the medical school of a large American university. Subjects - One hundred eighteen of 120 first-year medical student reports were analyzed. Two reports did not include any works cited and were excluded from the study. Methods - Over the course of 3 years, 15 20-minute library instruction sessions were conducted. The sessions, based on five clinical cases presented each year were conducted approximately two weeks before each report due date. Eighty-five case-specific resources were demonstrated, with teaching plans being modified from year to year based on the frequency of citation of a particular resource cited the prior year. A LibGuide online course guide also directed students to specific resources shown in the class, with content updated every year based on citation trends from the previous year. Every citation referenced in a report was then categorized into a) those that were discussed during an instruction session, b) those found on a course guide, c) those accessible through the library, d) those available from course material (i.e., PowerPoint presentation, lecture notes), or e) those which did not fall under any of the other categories. A citation could be included in multiple categories. Main Results – The 118 reports included 2983 citations. Over the 3 year period, an average of 77.51% of all citations were from library resources, 49.55% of the citations from a resource demonstrated in the class, and 21.68% from resources found in the course guide. Although citations from sources discussed in class did not increase significantly from year to year, the percent of citations from resources on the course guide significantly increased from 19.40% to 25.63%. Conclusion – Medical students cite library resources emphasized during instruction sessions.
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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 | 0.003 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".