Achieving excellence and acknowledging contribution: The 2018 <i>International Journal of Older People Nursing</i> Awards
Bibliographic record
Abstract
We count ourselves lucky, as the editorial team of International Journal of Older People Nursing (IJOPN), lead a journal occupying a special place in nursing and health care.We are the sole explicitly international gerontological nursing journal with a growing reputation, given our 2018 impact factor of 1.446, for high-quality work that makes an impact in science and care.As a result, we take pride in reviewing and publishing work from nurses and their colleagues completed by gerontological and other nursing specialties as well as from across health and social care disciplines.Publishing the best of those scholarly works requires excellent authors, exceptional peer reviewers and deeply dedicated Editorial Board members.Now, for the fourth year, we recognise those authors, reviewers and-for the first time this year, Editorial Board Members for their superb contributions to IJOPN.This year, we worked with our terrific International Awards Committee to take on the challenging task of selecting among the papers published in IJOPN during 2018.
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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 | 0.029 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.032 | 0.012 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".