Health literacy in childhood and adolescence. A bibliometric analysis of scientific publications and professionals’ involvement
Bibliographic record
Abstract
Background: Health Literacy (HL) is a powerful tool to empower children and adolescents in their own health. School nurses are the professionals who, with their expertise in health promotion and education, can facilitate this literacy throughout the educational process. Purpose: To analyze the scientific production in HL in childhood and adolescence in the last two decades, and to determine the involvement of professionals in this field, with emphasis on nursing professionals. Methods: A bibliometric analysis of the scientific literature (from 2000 to 2021) of articles retrieved from the Web of Science database was carried out. Original articles in all languages were considered as inclusion criteria. Bibliometrix 3.1.4 package from RStudio and VOSviewer were used to analyze publications and explain main results about citations, authors, countries, keywords trends, evolution, clusters of related terms, and professionals' involvement. Results: A total of 2032 articles were included in the analysis. The results of the analysis showed that both publications and citations increased substantially since 2014. The most prolific authors in this field are not the most cited so far. The countries that published the most during the period evaluated were the United States, Australia and Canada. The keyword clusters identified in this scientometric study made it possible to determine hotspots in the study of HL in childhood and adolescence, with "mental health" being one of the main terms identified. There are different health-related professionals who are publishing in this field; in spite of this, nurses are not visible as authors in the publications. Conclusion: The scientific literature on HL in childhood and adolescence is a growing field in which different professionals are involved. Despite the increase in the number of publications, and despite the role that nurses play in schools regarding HL, their presence in scientific production is practically nonexistent.
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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: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.130 | 0.214 |
| Science and technology studies | 0.001 | 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".