Bibliographic record
Abstract
PURPOSE: To replicate a 1992-1993 study of the characteristics of English-language nursing journals originating in countries other than the United States and to compare findings. Such information heightens awareness of publishing opportunities globally and enhances dissemination of information throughout the world. DESIGN: Descriptive survey with a questionnaire mailed to 159 editors of nursing and nursing-related journals. Data about the publication year, 1995, were collected from April 1996 through March 1997. METHOD: A 38-item questionnaire pertaining to journal, readership, manuscript review, and journal staff characteristics was used. FINDINGS: Information about 82 journals from 13 countries was collected with an overall response rate of 55%. In the 1992-1993 study the United Kingdom, Canada, and Australia accounted for the largest percent of publications. With few exceptions, results of the 1996-1997 and the earlier survey are remarkably similar. Differences include a higher total circulation, changes in circulation among journal categories, and more publications offering services to authors. Two main reasons for manuscript rejection continue to be that a manuscript is poorly written or poorly developed. CONCLUSIONS: Increased awareness of non-U.S. publishing outlets can lead to the acceptance of informative and well-written manuscripts and ultimately to the dissemination of information and knowledge.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".