Analyzing facilitators and barriers to telehealth in rural North America during the covid-19 pandemic: A scoping review
Bibliographic record
Abstract
Background: It has previously been established that many people living in rural communities experience health disadvantages and less access to medical care. Over the past decade, innovations in telehealth and other innovative models of care have been developed with the goal of overcoming these inequities for those living in rural areas. Objective: The aim of this paper was to describe both outcomes and characteristics of studies involving telehealth in rural areas of North America during the COVID-19 pandemic. Materials and Methods: A scoping review was undertaken. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was utilized in order to understand the empirical and theoretical data on telehealth usage in the United States, Canada and Mexico during the COVID-19 pandemic. The following terms were utilized: ‘rural health’ AND ‘telehealth’ AND ‘covid-19’. Separate searches were completed for the three included countries: ‘United States’, ‘Canada’, and ‘Mexico’. PubMed and Google Scholar were utilized. Results: The literature search revealed 1197 articles published in English between 1st January 2019 and 31st August 2022. One hundred and fifty articles were included in the review including 135 from the United States, 12 from Canada, and 10 from Mexico. Some articles were cross-collaborations between two of these countries. Among these papers, 18% (27) focused on telemedicine for mental health treatments, 14.7% (22) focused on oncology or cancer, 11.3% (17) focused on telemedicine for the veteran subpopulation, 2.7% (4) used a mixed methods approach, and 14% (21) used a qualitative approach. Conclusion: This scoping review reveals that the current literature on telehealth in rural areas during the COVID-19 pandemic is largely descriptive. There were only a few publications that focused on comparative health outcomes using telehealth in urban and rural populations in close proximity to each other. Telehealth is well represented in published literature on inequities and innovation, but there is still limited data on health outcomes and comparisons that can be drawn cross-nationally. Further studies should aim to study longer term health outcomes for those in rural areas using telehealth as opposed to areas where telehealth interventions have not yet been adopted
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".