Bibliographic record
Abstract
The 2nd Evidence in Practice Award is now open for entries. In approximately 750 words, can you describe a case study where your work has influenced clinical practice? We are looking for examples of good evidence-based librarianship practice in a healthcare setting, examples of where librarians and information professionals have influenced clinical practice and patient outcomes. The competition is open to partnerships of clinical and health professionals in the UK. The winning partnership will each receive a Personal Digital Assistant, £500 each towards attendance at a professional conference or course of their choice, plus a free delegate place at the 3rd Clinical Librarian conference. Prizes are jointly sponsored by NLH and BMJ Group. Entries can be submitted up to 31st March 2007 after which anonymised case studies will be judged by an independent panel combining clinical and information expertise. The judges' decision will be final. The award will be presented at the 3rd UK Clinical Librarian Conference, 11th & 12th June 2007, St William's College, York Minster, where the award winners will an opportunity to share their example of successful practice. Online entry for the award is available at: http://www.insitefulsurveys.com/Survey.asp?SI=110406111828
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.
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 | 0.030 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.416 | 0.213 |
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".