The rules for the publication of Spanish nursing journals
Bibliographic record
Abstract
Objective: To analyze the rules of publication of the Spanish nursing journals. Methods: Cross-sectional observational study, conducted in forty Spanish nursing journals, which used a questionnaire with 16 items. Results: Half of the analyzed present electronic journals and nearly 30% have followed an irregular quarterly and semiannually. 97.5% of journals have a section on the types of articles and a section with general rules, and 82.5% have a section with rules for submission of illustrations, pictures, etc. and the mechanism of selection of items. The most common style for citation and bibliography is Vancouver. More than half have a section explaining the structure of the articles, 65% have no model letter of assignment of copyright and 72.5% do not have a checklist. Conclusions: Most of the journals studied have in their rules on publication a section with the types of items that can be published to follow general rules and rules for submission illustrations, pictures, etc. On the other hand, slightly more than half of the magazines have a section with the structure to be writing an article, and a considerable percentage of them do not have a sample letter of copyright assignment and a list of checks, respectively.
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 | 0.043 | 0.224 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".