Research, development, and production of information resources and tools
Bibliographic record
Abstract
Purpose The Health Information Research Unit (HIRU) at McMaster University in Hamilton, Ontario, Canada, was founded to meet the information needs that were quickly becoming apparent in the course of teaching critical appraisal methods for the practice of evidence‐based medicine. Librarians have worked in HIRU from its inception and their activities include teaching searching skills to clinicians, collecting and analyzing data for developing methods hedges to increase retrieval of high‐quality clinical articles, evaluating study methods of articles for four evidence‐based journals, and developing more sophisticated ways to deliver electronic information to clinicians. This article summarizes these developments. Design/methodology/approach To show the range and richness of the roles performed over the years, some of the cycles and projects with which HIRU has been involved and how librarians have fit in are described. Not all projects have been “successful”, but often the unsuccessful ones are as informative as – if not more so than – those that met one's expectations. Findings One of the main outcomes of the work is the recognition that many job opportunities in areas of informatics exist for librarians and other information professionals. Other groups are acknowledging that librarians can have substantial input into information products and anyone considering health sciences librarianship as a career should realize that many of the skills obtained during training will equip one for work in many non‐traditional settings. Originality/value It is felt that a contribution has been made to the continuing education of many of one's peers and help given to build one's research base and information products. The professional relationships that have been established because of these interactions have been rich and rewarding. Anyone interested in a career in health librarianship is encouraged to consider the opportunities available in research and development and other non‐traditional (non‐library) settings.
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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 | 0.086 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.028 | 0.026 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".