Bibliographic record
Abstract
It is with great pleasure that the Research in the Workplace Award (RIWA)* assessment panel announces the winning proposal in 2006/2007 to be for a multi-site randomised controlled trial to determine the impact of providing a virtual reference service (Access Specialist Knowledge - ASK) to the local Primary Care and Mental Health Trusts within the UK National Health Service. The project will be led by Rachel Southon, Royal Surrey County Hospitals NHS Trust, in collaboration with Vicki Veness, also of Royal Surrey County Hospitals NHS Trust and John Loy, Avon & Wiltshire Mental Health Partnership NHS Trust. The project will commence in April 2007 and the assessment panel believes it will yield measurable outcomes and provide an evidence base for developing services to primary care. The project is due for completion by March 2008 and will be followed by a comprehensive programme of dissemination. A copy of the winning proposal can be accessed via the RIWA* web site at: http://ifmh.org.uk/RIWA.html. The proposal was considered to be well planned and an exemplary model of a proposal in terms of identifying an important question and an appropriate methodology with which to address it. RIWA* is a biennial award and details of projects which have previously been funded, together with news of future awards, can be found on the RIWA* web site at: http://ifmh.org.uk/RIWA.html. RIWA 2006/2007 is managed by IFM Healthcare and is sponsored by National Library for Health CPD Forum, IFM Healthcare, the Health Libraries Group, the University Medical School Librarians Group, and the University Health Sciences Libraries and Libraries for Nursing.
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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 | 0.076 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.067 | 0.043 |
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".