Integrating information literacy in health sciences curricula: a case study from Québec
Bibliographic record
Abstract
OBJECTIVE: To portray an information literacy programme demonstrating a high level of integration in health sciences curricula and a teaching orientation aiming towards the development of lifelong learning skills. The setting is a French-speaking North American university. METHODS: The offering includes standard workshops such as MEDLINE searching and specialised sessions such as pharmaceutical patents searching. A contribution to an international teaching collaboration in Haiti where workshops had to be thoroughly adapted to the clientele is also presented. Online guides addressing information literacy topics complement the programme. RESULTS AND EVALUATION: A small team of librarians and technicians taught 276 hours of library instruction (LI) during the 2011-2012 academic year. Methods used for evaluating information skills include scoring features of literature searches and user satisfaction surveys. DISCUSSION: Privileged contacts between librarians and faculty resulting from embedded LI as well as from active participation in library committees result in a growing reputation of library services across academic departments and bring forth collaboration opportunities. Sustainability and evolution of the LI programme is warranted by frequent communication with partners in the clinical field, active involvement in academic networks and health library associations, and reflective professional strategies.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".