Information Literacy and Librarians’ Experiences with Teaching Grey Literature to Medical Students and Healthcare Practitioners
Bibliographic record
Abstract
The concept of information literacy, which describes the knowledge and skills required in all contexts (i.e. educational sectors, the workplace), as well as in people’s everyday lives in today’s information rich society, was introduced in the United States in the early 1970s. According to the Association of College and Research Libraries Information Literacy Competency Standards for Higher Education (2000), it has been concluded that an information literate individual is able to determine the extent of information needed, access information efficiently, evaluate information and its sources critically, and use information effectively. Information literacy skills become even more central to meeting the requirements of dealing with complexity and large volumes of information from grey literature. Our interests as health sciences librarians and thereby the focus of this paper lie in portraying the unstructured nature of grey literature and discussing methodologies and approaches towards teaching this elusive material to those in the health sciences sector, particularly medical students and healthcare practitioners, clients we serve within the Health Information Network Calgary. The Network was formed in 2005 through fee-for-service contracts between the University of Calgary and two partners, the Calgary Health Region and the Alberta Cancer Board. An integrated health knowledge service is provided for healthcare practitioners, staff, patients, and families from Knowledge Centres at major acute care sites, with the University of Calgary Health Sciences Library serving as the Network hub. In both medical school contexts and workplace settings, such as acute care facilities, information literacy is closely associated with the ability to acquire and develop competencies to enable individuals to think critically and use information appropriately. Giving the end user knowledge related to research information, widening his/her horizons, and implementing critical thinking and carefulness in using information, is more essential than instruction on how to search various information resources. In our own teaching we employ casebased problem-based learning, described by L. Carder, P. Willingham and D. Bibb (2001). We have found this method more effective, active and more student-centered, as it falls in line with a general trend in education, which focuses on making our users independent lifelong learners, and also fits our service goals within the Health Information Network in meeting the needs of medical students and healthcare practitioners.
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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 | 0.042 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".