Guiding the Grey: The Implementation and Evaluation of a Journal Club amongst a Librarian and Clinical Practice Guideline Developers – A Cancer Care Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Guideline Utilization Resource Unit (GURU) is composed of knowledge management specialists (KMS) and nurse facilitators (NF) who support multidisciplinary teams in developing, implementing, and evaluating provincial clinical practice guidelines (CPGs) for the diagnosis, staging, treatment and follow-up of cancer. These CPGs are evidence-based documents with consensus recommendations; they are freely available on a public website for access by practitioners and patients, and are a form of grey literature. Team members at GURU consult regularly with the librarian to ensure that the most accurate and comprehensive search strategy is used to develop CPGs. The goal of this project is to describe the process of organizing and evaluating a journal club involving a unique collaboration between guideline developers and a librarian.The journal club is comprised of three KMSs, two NFs, the GURU Manager and an embedded librarian. The group has been meeting once per month since April 2012. Each member takes turns selecting two articles related to CPG development or implementation, and is responsible for leading an informal discussion. To evaluate the usefulness of the journal club and the impact of grey literature on CPG development in Alberta, all members of the journal club (n=7) were interviewed in a focus group setting or a semi-structured interview. Transcripts of audio-recorded interviews will be qualitatively analyzed for repeated themes related to knowledge gained from, and perceived benefits of journal club meetings. This datasets contains these transcripts.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it