Telehealth as a health education tool in the fight against the new Coronavirus: a systematic review
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 epidemic started in 2019, leading to a World Public Health emergency. As a result, the typical evaluation of patients was limited, resulting in an increased demand for virtual activities, making it necessary to use resources to reach the general population. Telehealth has been a tool widely used in this period to increase the resolution of Primary Care and provide the strengthening of health services and care provided to the population. OBJECTIVE: To analyze the effectiveness of telehealth in the educational health process in combating COVID-19. MATERIAL AND METHODS: The review was initiated by the acronym PECOS. The inclusion criteria were studies published in 2020, descriptive and complete. Exclusion criteria were duplicates, review articles, letters to the editor or articles that do not address the topic. The articles were published in Pubmed, Science Direct, Scopus and Web of Science databases. The StArt tool was used to manage the articles and Zotero, the reference manager. The selected articles underwent quality analysis and risk of bias, based on the Newcastle Ottawa checklist and were sent for data extraction. RESULTS: Several countries have applied telemedicine and obtained favorable results, even though telehealth activities cannot replace contact with the patient, it can serve to reduce the workload of health professionals and offer remote clinical services, such as diagnosis and monitoring. The tool enables case management, intermediate activities, such as health education, premature access to patients, in addition to reaching vulnerable populations. For most patients, the platform can be used for emotional support, counseling and guidance, which includes computerization on prevention, analysis of conducts to be performed and the identification and execution of necessary interventions, in addition to monitoring the health study. Enabling cases that do not require instant treatment to avoid external contact and allow certain empowerment of knowledge by the individuals who are assisted. CONCLUSION: Telehealth became necessary and more used with the worsening of the world situation caused by COVID-19. Although those who use it face some problems, such as lack of necessary structure and lack of experience, the results are positive because it continues to be promising and effective to continue patient care. In addition, it can trigger a paradigm shift in the provision of health services and an alternative support for health education actions.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".