Bibliometric Analysis of Studies on Smoking Cessation in the Field of Nursing
Bibliographic record
Abstract
The bibliometric analysis method was used to evaluate the articles both qualitatively and quantitatively and to visualize the data. “Web of Science Core Collection” database and the VOSviewer program were used to obtain and analyze the data. Distribution of studies by year, author country institution analysis, journal and author citation analysis, journal and author co-citation analysis, and keyword analysis was carried out. Two hundred authors representing 45 countries and 200 institutions contributed to a total of 285 studies on smoking cessation published in 84 journals between 2003 and 2023. While Journal of Addictions Nursing was determined as the journal in which the largest number of articles were published, Research in Nursing and Health was determined as the most cited journal. The top three countries that provide the most support for published articles are the United States, Australia, and Canada. The five most frequently used keywords in the published studies were “smoking cessation,” “smoking,” “pregnancy,” “nurses/nursing,” and “tobacco.” The results of this bibliometric analysis showed that the researchers have had an increasing interest in smoking cessation in the nursing field over the past 20 years. It is recommended to focus on less-studied topics in studies to be planned on smoking in the field of nursing.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.038 | 0.095 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".