A BASELINE STUDY OF THE FACTORS THAT SHOULD BE USED TO MEASURE THE EFFECTIVENESS OF TELEMEDICINE
Bibliographic record
Abstract
Alaska is an expansive and complex geographic area with many rural communities of small villages and few towns in remote locations. In addition, inadequate healthcare facilities in these locations makes health care management a challenging task in state of Alaska. Therefore, it is important to find an effective alternative, such as telemedicine, for delivering health care in remote regions of rural Alaska. The objective of this paper is to create a baseline result from pretelemedicine data that is important in measuring telemedicine effectiveness after its implementation. In this paper, we focus on data gathered before implementation of telemedicine to identify factors significant to patients' satisfaction. Four different regions: Maniilaq, Norton Sound, Yukon Kuskokwim, and Bristol Bay Area are selected for implementation of telemedicine. Regression analysis is used for identifying factors that are important to patients' satisfaction. Regression models are estimated separately for each region, and a separate model is used for all regions combined. The most important factor revealed from analysis in terms of magnitude and statistical significance is the perceived level of prescribed treatment across all regions. The amount of time that health care provider spent with patient is also emerged as an important factor. Another important factor is ability to communicate. All these factors are found statistically significant regardless of geographic region considered in this paper. Therefore, these important factors may be used for post-telemedicine effectiveness measure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".