New Directions in Health Sciences Libraries in Canada: Research and Evidence based Practice Are Key
Bibliographic record
Abstract
This article is the second in a new series in this regular feature. The intention of the series is to look at important global developments in health science libraries. These articles will serve as a road map, describing the key changes in the field and exploring factors driving these changes. The present article by two Canadian librarians identifies important national developments which are shaping the profession such as the centralisation of health care services, the challenge of providing consumer health information in the absence of a national strategy, government recognition of the need to recognise and respond to the health needs of indigenous peoples and the growing emphasis on managing research data. Although their profession is strong, health science librarians must find ways of providing enhanced services with fewer staff and demonstrate value to organisations.
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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 | 0.063 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.021 | 0.029 |
| Scholarly communication | 0.038 | 0.020 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".