Effectiveness and Barriers of Telehealth Services During COVID-19 Pandemic
Bibliographic record
Abstract
The coronavirus outbreak has introduced many challenges for the health-care delivery system, its workers, and health-care recipients. To overcome the challenges coming up during the coronavirus disease-2019 (COVID-19) pandemic, health-care sector was majorly helped by telehealth, e-health, and technologies involved in consultation, diagnosis, and treatment of patients from a distance. However, it has own benefits and barriers, which are discussed in this review. This review has been conducted through searching five databases including PubMed, ResearchGate, Google Scholar, Cochrane, and ScienceDirect. Inclusion criteria included studies clearly defining any use of telehealth services during COVID-19 pandemic and its effects and barriers, written in English language, published from 2019 to till date, and including studies from different countries. Narrative synthesis was undertaken to summarize and report the findings. Ten studies met the inclusion criteria out of the 97 search results. The articles included in our studies showed a significant increase in the uptake of telehealth services during this COVID-19 pandemic. Countries like the U.S.A showed an 80% decline in-person visits among the Canadian population 41% of them wanted virtual visits compared to in-person visits. The patients have reported high satisfaction with telehealth services according to the related studies although have reported hindrances and potential barriers to it like limited access to Internet availability, devices, lack of awareness about technology, high cost for implementation, and legal framework related to policies that includes privacy and confidentiality. Based on the findings of this review study, telehealth has been found as an effective way of health delivery system in these difficult times, but there are certain factors and issues related to its use which need to be looked upon. This narrative review indicates that the use of telemedicine and telehealth services during this COVID-19 pandemic has a plan of much help, as when compared to the barriers, it may produce to reach a large population at their home without putting the lives of health-care workers and the patients themselves at risk.
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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 | 0.016 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".