Globalizing nursing science: analysis of nursing’s participation in the open access movement from 1993 to 2014
Bibliographic record
Abstract
Background: The traditional journal subscription model restricts access to scholarly information since proprietary fee-based databases charge high subscription fees, do not provide access to all journals in the same geographic region, and include minimal access to research journals from other countries. This practice insulates nursing knowledge, causes duplication rather than replication of research, and results in a lack of breadth and depth to our science.Objective: Describe the state of nursing participation in the Open Access (OA) movement.Methods: Using a descriptive, exploratory approach, all nursing journals in the Directory of Open Access Journals (DOAJ) data warehouse were extracted, tagged, and analyzed.Results: Sixty-two nursing journals from 23 countries have registered as Open Access. Brazil publishes the largest number of OA nursing journals (14), followed by the U.S. (9) and Spain (9). Two countries publish four OA nursing journals (Canada, Iran), while the remaining 18 countries publish one or two OA nursing journals. Fifty percent publish in either Spanish, Portuguese, or Spanish/Portuguese, while another one-third (32%) publish in English. Importantly, 82% of OA Nursing journals do not require article processing charges; of those who do have APCs, the majority (66%) are $300 or less.Conclusions: Although nursing participated early in the OA movement, growth has been consistent but sluggish. Neither the size of the country nor economic status seem to have a strong influence on decisions to produce OA nursing journals. Encouraging participation in OA will advance the science of nursing by allowing broader and more coordinated access to information to the global community.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".