Librarian-Led Information Literacy Training Delivered in Small Groups Improved Medical Students’ Confidence in Their Ability to Use Evidence Based Resources Effectively
Bibliographic record
Abstract
A Review of: McClurg, C., Powelson, S., Lang, E., Aghajafari, F., & Edworthy, S. (2015). Evaluating effectiveness of small group information literacy instruction for Undergraduate Medical Education students using a pre- and post-survey study design. Health Information & Libraries Journal. 32(2), 120-130. http://doi.org/10.1111/hir.12098 Abstract Objective – To assess the effectiveness of librarian-led small group information literacy sessions, which were integrated into the second year of a three-year undergraduate medical course. Design – A pre- and post-intervention survey questionnaire. Setting – A large university in Canada. Subjects – A cohort of 160 second year undergraduate medical students enrolled in the three-year programme of a large university in Canada. Methods – As part of the redevelopment of the undergraduate three-year medical course, information literacy skills in evidence based medicine were integrated into the seminar and small group teaching programme. Every week for five weeks, 3 librarians each visited 4 small groups of 15 students to deliver a 15-minute session as part of a 2-hour long seminar led by practising physicians. The sessions did not include a formal hands-on component, however, students were encouraged to try out searches on their own devices. Each 15-minute session covered 3 learning objectives, including how to use PubMed clinical queries, how to use MeSH, and how to search for systematic reviews and guidelines. A pre- and post-intervention survey design was used to assess students’ perceptions of the impact of these sessions. The students were asked to complete an online Survey Monkey survey before and after the five week lecture block. The questions covered resource selection, perception of barriers to finding evidence based information, and the students’ confidence in using evidence based resources. The data were analysed descriptively. Main results – The pre-survey achieved a 90% (144/160) response rate while the post-survey achieved a 75% (112/160) response rate. The post-survey indicated an increase in the likelihood that students would use Ovid MEDLINE, carry out a literature search, and consult a librarian, with a decrease in those who would consult a print or online textbook. There was limited change in the students’ confidence that they could find answers quickly, but more of an increase in the proportion of students who were confident they could find systematic reviews and guidelines, and use search limits, PICO, and MeSH. Before the intervention, “knowing where to search,” devising a search strategy, and retrieving too many results were all thought to be obstacles by the students. After the small group training, students considered these issues less of a problem. The post-survey also included an opportunity for the students to comment on their experience with the programme overall. Of the 54 responses received, 34 identified the library component as being the most important thing they had learned in the small group part of the course. Conclusion – The authors conclude that integrating information literacy into the undergraduate curriculum as part of the small group seminar series is effective. They suggest future directions for research, such as a study to assess the impact of the training on specific skills rather than student confidence and evaluations of other teaching methods.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".