Research-Embedded Health Librarians as Facilitators of a Multidisciplinary Scoping Review
Bibliographic record
Abstract
Program objective: To advance the methodology and improve the data management of the scoping review through the integration of two health librarians onto the clinical research team. Participants and setting: Two librarians were embedded on a multidisciplinary, geographically dispersed pediatric palliative and end-of-life research team conducting a scoping review headquartered at the British Columbia Children’s Hospital Research Institute. Program: The team’s embedded librarians guided and facilitated all stages of a scoping review of 180 Q3 conditions and 10 symptoms. Outcomes: The scoping review was enhanced in quality and efficiency through the integration of librarians onto the team. Conclusions: Health librarians embedded on clinical research teams can help guide and facilitate the scoping review process to improve workflow management and overall methodology. Librarians are particularly well equipped to solve challenges arising from large data sets, broad research questions with a high level of specificity, and geographically dispersed team members. Knowledge of emerging and established citation-screening and bibliographic software and review tools can help librarians to address these challenges and provide efficient workflow management.
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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 | 0.433 | 0.435 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.007 | 0.038 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".