The Influence of Health Technologies in Managing Chronic Illness: Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives: This research aims to find the influence of health technologies in managing chronic illness through a systematic review. Method : structured around a comprehensive systematic review, aiming to gather, assess, and synthesize empirical data on the impact of health technologies on chronic disorder management. Utilizing databases such as PubMed, Medline, Scopus, Google Scholar, and Web of Science, a meticulous search strategy was crafted using relevant keywords and Medical Subject Headings (MeSH) terms. To ensure inclusivity, manual searches of reference lists were also performed. The review was confined to English-language articles published between 2013 and 2023.Result: Health technologies, including mobile applications, telemedicine, and artificial intelligence, were found to have a significant positive impact on the self-management and monitoring of chronic conditions. Patients reported enhanced convenience, a sense of control over their health, and identifying early signs of exacerbation in chronic illnesses, hence facilitating proactive healthcare and mitigating the need for hospital admissions.Conclusion: Healthcare organizations aiming to adopt or improve the utilization of health technology for managing chronic illnesses should give priority to user-centered design and data protection. Adapting digital solutions to be user-friendly and easily available to individuals of all ages and technological abilities is of utmost importance.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it