Will telemedicine survive after COVID-19?
Bibliographic record
Abstract
During the last 20 years, telemedicine has evolved in Quebec despite several barriers. We studied how a university health centre in Montreal implemented a strategy to enhance the use of telemedicine with the deployment of appropriate infrastructures, continuous training, and the use of advanced technologies, before and during the pandemic. COVID-19 accelerated the use of telemedicine by overcoming some pre-existing barriers. However, telemedicine was mainly limited to a distance consultation during the pandemic using telephone calls or videoconference. The future of telemedicine depends on lifting these obstacles. We need to better define telemedicine and in-person medicine to guarantee the quality of medical and professional acts. We propose some strategies to achieve these goals, combining cultural change, continuous training, new technologies to improve quality of care, and a vision of healthcare with telemedicine oriented on value creation.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".