Information Skills Survey: Its Application to a Medical Course
Bibliographic record
Abstract
Objective - To test if the Information Skills Survey(Catts Information Skills Survey for Assessment of Information Literacy in Higher Education) is suitable for the purpose of investigating the information literacy levels of a group of students in medicine. If not, the study was designed to determine the modifications that are necessary to make the Information Skills Survey a reliable instrument for investigating the information literacy levels of a group of students in medicine.
 
 Method - Administration of the Information Skills Survey to two groups of medical students. To confirm the validity of the results, follow up questions and interviews were also conducted. Statistical analysis was carried out to determine the internal consistency of the questions in relation to the Information Literacy Standards and also to determine the statistical significance of the results.
 
 Results - The two groups of students reported similar results for a number of the tested skills. However, several areas of difference were also identified. The main areas of difference between the two groups were the questions that can be interpreted as being related to clinical practice. This was also emphasised in the interviews.
 
 Conclusions - The Information Skills Survey is a useful tool to investigate the information literacy skills of groups of medical students who are in their early years of study. Further research needs to be done to develop valid questions for medical students in the clinical years. This would reflect the different information resources that are used in clinical practice.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".