Medical Degree students' use of information: From writing and citing to evidence assessment
Bibliographic record
Abstract
Objective: The aim of this study was to assess information literacy performance of students doing their master thesis at 5th year the Medical Degree Programme, Lund University, Sweden. The study investigates if there is a difference in performance between library class participants and non-participants. Method: A case-control approach with rubrics assessment was used to assess students’ information literacy performance in 26 selected master theses. 13 theses were selected from class participant group, and 13 theses from the non-participant group in a blinded process. The rubrics were based on the formal assessment rubrics of the course. Rubrics related to information literacy learning objectives and class content was further developed with more detailed indicators. The Mann-Whitney U-test was used for statistical analysis. Result: The case group usually outperformed the control group, with a few exceptions (p=0,650). The use of original articles and presenting all references in the reference list was equal among the groups. In using adequate number of references, using sources relevant to aim, using evidence hierarchy, synthesizing references with results and using Vancouver style correctly the case group performed better. In using references for method description and using previous references in the discussion the control group performed better. Conclusion: Students need more support in selecting high quality references, using references to describe methods, the importance of referring to all sources and to use the Vancouver style correctly. The results of the study will be used to develop the library instructional classes further.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".