Analyze the Effectiveness of Telemedicine in Providing Healthcare Services in the United States
Bibliographic record
Abstract
The United States (US) government spending on the healthcare system is one of the highest in the world. However, the US healthcare outcome compared to other OECD countries is below average. This is caused by the number of patients visiting healthcare services remain low compared to the OECD countries due to the following reasons: affordability, allocation of healthcare services, and accessibility. The lack of universal coverage in the US healthcare system poses a challenge of affordability to patients. This led to a high out-of-pocket spending on healthcare services and patients will choose to skip or delay the treatment. Allocation of healthcare services such as the lack of patients and the hospital beds caused a high waiting time and caused patients unwillingly to access to healthcare services. The long-distance travel in the rural area led to low accessibility to healthcare services. Telemedicine is a method that allows the delivery of healthcare services remotely using technology that can solve the US healthcare problems. The adoption of telemedicine in the US healthcare system has experienced a substantial increase during the period of Covid. The analysis on the effectiveness of using telemedicine to provide healthcare services in the US healthcare system will be conducted based on factors related to the existing challenges.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".