Determining the information literacy needs of a medical and dental faculty
Bibliographic record
Abstract
Introduction: The Faculty of Medicine and Dentistry at the University of Alberta is large and diverse. Liaison librarians at the Health Sciences Library decided in late 2009 to undertake a system-wide evaluation of the information literacy (IL) instruction being delivered to the Faculty. The goals of the evaluation were to identify current strengths and gaps in instruction, to realign teaching priorities, and to inform the development of effective asynchronous Web-based delivery mechanisms, such as interactive tutorials, to support the Faculty's move to electronic course delivery. Methods: The main data collection method was a survey of different user groups in the Faculty, including undergraduate and graduate students, residents, and faculty. Secondary data included a literature review, consultation with key collaborators and analyzing program documents. Results: All undergraduate medical students receive IL instruction. Fewer than a third of graduate students, only half of residents, and a small fraction of faculty, receive instruction. The current curriculum needs to be revised to be less repetitive. Most respondents wanted to receive training on advanced database searching, and preferred in-person instruction sessions. Web-based tutorials were the next most popular mode of delivery. Discussion: This study is one of the few medical information literacy surveys that used a broad, strategic approach to surveying all user groups at a medical school. These data provide a baseline overview of existing instruction across user groups, determine potential need for IL instruction, provide direction for what should be taught, and identify preferred methods for delivery of a comprehensive training program centered on Faculty needs.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".