Adopting and Implementing Telehealth in Canada
Bibliographic record
Abstract
Canada spans 9,976,140 square kilometers and has an approximate population of 32 million people (Statistics Canada, 2001). More than 90% of Canada’s geography is considered rural or remote (Government of Canada, 2001). Despite the highly dispersed population, and, indeed, because of it, Canada is committed to the idea that a networked telehealth system could provide better access and equity of care to Canadians. Growing evidence of the feasibility and affordability of telehealth applications substantiates Canada’s responsibility to promote and to develop telehealth. Telehealth is the use of advanced telecommunication technologies to exchange health information and provide healthcare services across geographic, time, social, and cultural barriers (Reid, 1996). According to a systematic review of telehealth projects in different countries (Jennett et al., 2003a, 2003b), specific telehealth applications have shown significant socioeconomic benefits to patients and families, healthcare providers, and the healthcare system. Implementing telehealth can impact the delivery of health services by increasing access, improving quality of care, and enhancing social support (Bashshur, Reardon, & Shannon, 2001; Jennett et al., 2003a). It also has the potential to impact skills training of the health workforce by increasing educational opportunities (Jennett et al., 2003a; Watanabe, Jennett, & Watson, 1999). Therefore, telehealth has a strong potential to influence improved health outcomes in the population (Jennett et al., 2003a, 2003b). Fourteen health jurisdictions—one federal, 10 provincial, and three territorial—are responsible for the policies and infrastructure associated with healthcare delivery in Canada. This article presents a telehealth case study in one of Canada’s health jurisdictions—the province of Alberta. The rollout of telehealth in Alberta serves as an example of best practice. Significant milestones and lessons learned are presented. Progress toward the integration of the telehealth network into a wider province-wide health information network also is highlighted.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".