Scientific Mapping of Papers Related to Health Literacy Using Co-Word Analysis in Medline.
Bibliographic record
Abstract
Background and Objective: It is necessary to study the emerging trends and areas of knowledge and to predict the direction of future research on the concept of health literacy in order to promote community health.Therefore, this study was conducted with the aim of mapping research knowledge in the field of health literacy and showing the structure of knowledge and their evolution over time. Materials and Methods:This scientometrics research was conducted using Co-word analysis technique includes the steps include: Data retrieval, Data analysis and Mapping and Visualization Papers from the MEDLINE database during retrieved and analyzed.The bibliographic data analyzed using the visualization of similarities technique by text-mining and visualization techniques of VOSviewer.Results:A total of 7,429 documents, growth of scientific publications related to the field of health literacy has been increased rapidly over the past 10 years and continues to grow in recent years.The stronger studies were mainly conducted at research institutions of higher education in the Canada and United States.Four Core authors groups with a higher influence were identified.The keyword of "communication", "depression", "health education", and "internet" had respectively the highest frequency.The results of cluster analysis identified and categorized them into three major clusters.Health literacy field had a close relationship with the lifestyle dimensions, health information technology, mental health literacy and chronic diseases.Conclusions: Given the interdisciplinary nature of health literacy the areas of education, health, Information Communication Technology (ICT) science, and mental health will help the cooperation of experts in these areas to enrich scientific research and make them more applicable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.195 | 0.158 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".