Analysis of research status quo,research hotspots and research frontier of mobile health and its inspiration to nursing
Bibliographic record
Abstract
Abstract Objective:To probe into the research status quo,research hotspots and research frontier of mobile health,so as to provide a theoretical basis for Chinese nurses carrying out mobile health related research and practice.Methods:Bibliometrics,coword analysis,cluster analysis,citation analysis were used,the mobile health related literatures in PubMed database,Science of Citation Index database were analyzed by using BICOMB and SPSS18.0 software.Results:In recent years,amount of documents in mobile health field showed a rapid growth trend.More than 70% of the total literatures were published in three countries including the United States,Canada and the United Kingdom.Mobile health research hotspots included research and development of mobile health equipment and systems.Mobile health was used for carrying out health education,health promotion,chronic disease management,cancer care,and mobile health measures were used to provide community health services.The research frontier included research on mobile health engineering technology,guidelines or specifications published by authoritative organizations in the field of mobile health,acceptance degree and preferences of users for mobile health,quality assessment of mobile health APP,effect evaluation of mobile health,thinking and review of mobile health.Conclusions:Mobile health was in the stage of vigorous development.Nursing staff in China should carry out mobile health research and practice according to international research hotspots,research frontier and clinical needs,and improve research level and research scope,so as to improve nursing quality.
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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 | 0.075 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.055 | 0.060 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".