Trends in nursing ethics research: Mapping the literature production
Bibliographic record
Abstract
BACKGROUND: There have been a number of debates in the field of nursing ethics. Researchers have focused on various aspects of nursing ethics, such as professional ethics, professional, nursing and ethical values. Within this research, a variety of literature reviews have been conducted, but to the best of our knowledge, bibliometric mapping has not yet been used. OBJECTIVE: This article aims to analyse the production of literature within nursing ethics research. RESEARCH DESIGN: In order to examine publishing patterns, we focused on publishing dynamics, prolific research entities and the most-cited articles. We additionally visualised the content of the literature using a novel mixed-method approach, combining bibliometric analysis and mapping with thematic analysis. Ethical considerations: In our study, ethical review was not required. FINDINGS: A total of 1416 information sources were found in the Scopus database. Overall, literature production has increased; however, in recent years, the quantity of published material has begun to decrease. The most prolific countries are the United States, the United Kingdom and Canada, and the most prolific source titles are Nursing Ethics, Journal of Advanced Nursing and Nursing Times. Lately, research in the field of nursing ethics has been focused more on life care (providing for the basic needs of older residents), moral distress and community nursing. DISCUSSION: The dynamics of research literature production showed an exponential rise in the number of published information sources - a rise which started in the period between 1974 and 1998. Since that period, the trend has stabilised, which might indicate that nursing ethics research is starting a transition to a mature phase. CONCLUSION: The innovative use of bibliometric analysis and mapping, together with thematic analysis, is a useful tool for analysis of research production in the field of nursing ethics. The results presented can be an excellent starting point for literature reviews and more exhaustive data, information and knowledge seeking.
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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 | 0.048 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.114 | 0.152 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".