Bibliographic record
Abstract
This is the second of three articles which explore trends in health science libraries. It is based on a series of articles called New Directions in Health Science Libraries published in a HILJ regular feature (International Perspectives and Initiatives) between June 2017 and March 2020. The series covered 12 countries: The United States, Canada, Australia, China, England, two countries in Africa (Uganda and Tanzania) and five in Europe (Sweden, Romania, Belgium, Germany, and Switzerland). The commissioning editor identified potential authors and invited them to write a short piece. They were given a briefing sheet which said: 'Your article should serve as a road map, describing the key changes in the field and explain the factors driving the changes'. A review of the 12 articles identified 11 trends. This is the article which explores the trends four trends, using examples provided by the authors. The trends covered are: Involvement in systematic reviews and data synthesis; Professional development for health science librarians; Providing education and training to students, researchers, and clinicians; Supporting the delivery of health literacy.
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How this classification was reachedexpand
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.040 | 0.308 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".