Bibliographic record
Abstract
This year, we launched our expansion to six issues per year! We have been able to accomplish this due to the increased number of strong manuscript submissions we have been receiving since we obtained an impact factor. We have also been successful at maintaining an impact factor over the last 3 years. We are currently ranked 62/106 in nursing with an impact factor of 0.870. This is due in part to the exceptional job our reviewers have done. We have also gone from receiving 50 manuscripts a year 6 years ago to over 130 a year. Due to this increased number of manuscripts, we have also had to make some changes in what type of manuscripts we are able to accept. Clinical Nursing Research’s (CNR) acceptance rate is now below 25%. Examples of manuscripts with less of a chance of being accepted for publication include under-powered studies, those not following the submission guidelines, international manuscripts that have not had a strong English edit, and manuscripts that do not focus on clinical research. Moving forward for next year, I would like to expand our panel of reviewers to enable us to decrease our review time for first decision from the current mean of 46 days to 30 days. This year, we started expediting decisions on submissions that were found not to be an appropriate fit or did not follow the submission guidelines. This allows authors to submit their manuscripts to a more appropriate journal in a more timely fashion. This past year, we have had manuscript submissions from 31 countries. We have published manuscripts from the following countries: Canada, China, Finland, Jordon, Korea, Philippines, Spain, Sweden, Taiwan, Turkey, and the United States this past year. I hope to see this international representation again this year.
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 | 0.044 | 0.135 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.034 | 0.016 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.162 | 0.179 |
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".