Re-visioning library support for undergraduate educational programmes in an academic health sciences library
Bibliographic record
Abstract
McMaster University’s Health Sciences Library (HSL) began to transition to a new liaison service model in early 2018. One of its librarians sought to understand how an academic health sciences library can optimise its support for academic undergraduate programmes. This scoping review of the literature was pursued with the aim to submit an informed recommendation to HSL’s new Education and Lifelong Learning team, so the library could shift its approach to information literacy instruction in a manner that would optimise its outcomes for students and improve relationships with faculty staff. The author searched seven databases: Library, Information Science & Technology Abstracts (LISTA), ProQuest ERIC, OVID Embase, EBSCO CINAHL, OVID Medline, Web of Science and PapersFirst. She developed a robust and comprehensive search strategy that used a combination of subject headings and keywords to describe information literacy, metaliteracy, libraries and health sciences education. The author also hand-searched bibliographies of seminal publications to broaden her search for relevant literature. The findings in this review indicate that metaliteracy as a concept has not been intentionally implemented into information literacy training at academic health sciences libraries. The review finds that it is preferable to integrate information literacy skills directly into course or programme curricula and align those skills with the evidence-based practice skills undergraduates are already learning. Further, establishing a programme that builds on these skills gradually throughout the duration of the academic programme, rather than one-shot library instruction, is also preferred. To achieve success, libraries must build strong collaborative relationships with faculty staff. The author provides recommendations for practice that reflect the findings of this review. Other academic health libraries may benefit from this review by taking into consideration its findings and subsequent recommendations.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.040 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".