Trends and current topics in child and family-centered care research: A bibliometric analysis and visual mapping
Bibliographic record
Abstract
This study aimed to examine the developmental trends and research focus of the child and family-centered care literature through a bibliometric analysis of articles published between 1990 and 2023. The bibliometric analysis method was used in the study. Data were obtained from the Web of Science Core Collection and Scopus databases. Bibliometric analysis and visualization were conducted using the R language, along with the bibliometrix and openxlsx libraries and the biblioshiny interface. The study included citation, co-occurrence, co-authorship, co-word analysis, and trend topic analysis. Between 1990 and 2023, a total of 1.613 publications related to child and family-centered care were published. Of these publications, 1.598 were original research articles, and the majority (%95) were written in English. The United States was the country with the highest number of publications and the most co-authorship collaborations. Collaborating countries included Canada, Brazil, Iran. Journal of Pediatric Nursing was the most prolific journal (n=149). The author with the highest number of publications on the topic was Shields (n=23). The most cited works related to child and family-centered care were by Gorski (2021), Kerr (2002), and Snow (2009). The institution producing the most publications on the topic was McGill University. In trend topic analysis, key terms such as ‘‘family-centered care’’, ‘‘nursing’’, ‘‘parents’’, ‘‘child’’, and ‘‘child-centered care’’ emerged. Child- and family-centered care research is widespread in developed countries but lacks interdisciplinary collaboration, particularly outside pediatric intensive care units. Future studies should promote co-authorship and international cooperation, providing healthcare professionals and researchers with insights into unexplored areas and opportunities for collaboration.
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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 | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.167 | 0.182 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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, 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".