Bibliographic record
Abstract
JanetMcCray and Cally Ward have constructed an extremely interesting and relevant contents list for this edition of JNM.Interprofessional issues, whether these relate to practice or education, are now high on the agenda and driving the way forward in both.Certainly, where clients straddle health and social care services, and health professionals are working within or across interprofessional teams, the issues raised in this edition identify many of the challenges still to be tackled in providing seamless and appropriate care.Both guest editors have a background in services for people with learning disabilities, an area of practice that is perhaps ahead of the game where interprofessional working is concerned.The lessons learnt from this specialty are relevant for many other areas of practice where interdisciplinarity is being encouraged.The content of this issue is an eclectic mix, ranging from practitionersÕ work in progress to scholarly articles and the presentation of a model for use in practice.It is just this balance of work, and viewpoint, that JNM is striving to achieve as it moves into yet another year.The focus of issues for 2004 span classical Ônurse manage-mentÕ issues as well as diversifying into nurse management of practice (pain management); an edition being constructed by a team from the Royal College of Nursing exploring Political Leadership; an issue on Research Governance and Practice Development and one looking at Nurse Practitioners.We start the year considering the topic of Patient and User Participationsee you there!
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.076 | 0.046 |
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".