Patient resource to support the utilization of virtual care at home in rural communities
Bibliographic record
Abstract
Background: Virtual care (VC) is a meeting between a patient and their health care provider using a form of technology that allows the patient and provider to be in different locations. Its use has increased since the onset of the COVID-19 pandemic but unfortunately, some rural areas in Newfoundland and Labrador (NL) did not adapt and increase their use of VC as quickly as those in urban areas. Purpose: To develop a patient resource to increase awareness and utilization of VC in a rural health area in the Western Health region of NL. Methods: A literature review was conducted to understand the barriers that existed and to explore any previous strategies and their effectiveness in relation to the implementation of VC visits among patients. Consultation interviews were then conducted with VC consultants and health care providers to determine if any previous strategies were successful in increasing the use of VC among patients. Results: According to the literature, several barriers exist that prevent the use of VC in rural communities, with the most common being a lack of awareness of VC and technology barriers. Effective interventions included providing VC devices to patients and technical support. The consultees reinforced that patient awareness on VC should be increased and technical support was necessary for successful VC visits. Pamphlets were previously used by the consultees to raise patient awareness and increase comfort levels prior to the availability of video VC from home and these were recommended for future use. Two pamphlets were developed; one provided general VC information to raise patient awareness of their ability to use VC and the other contained technical support information to build patients’ comfort level with the technology required for VC. Conclusion: The goal is for these pamphlets to be distributed in common public areas such as the hospital/clinic, grocery store, and pharmacy in rural communities in the Western Health region.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".