Our tips for you: Ideas for authors from the IJOPN Editorial Team
Bibliographic record
Abstract
We, the International Journal of Older People Nursing (IJOPN) Editorial Team—Editor in Chief Sarah Kagan; Associate Editors G.J. Melendez-Torres and Jennifer Baumbusch; and Social Media Editors Emma Blakey, Anna Carapellotti and Marleen Dohmen, are optimistic by nature and collaborative by practice. In the midst of so much upheaval around the world, we are taking steps small and large to improve the journal and better support authors, reviewers and readers. We recently updated the types of manuscripts we accept for submission to the journal. As we refined and confirmed our new list of manuscript types, we saw an opportunity to share with you—new and established authors—some of our best tips for undertaking sound research, analysing results and preparing manuscripts for submission. We categorised our tips for you and hope you find them useful. Thank you for considering IJOPN as a potential venue for disseminating your research and other scholarly work. We truly look forward to reading manuscripts you submit to the journal.
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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 | 0.079 | 0.304 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.040 | 0.022 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.015 | 0.031 |
| Insufficient payload (model declined to judge) | 0.018 | 0.022 |
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".