A “Not So Quiet” Revolution: Systemic Benefits and Challenges of Telehealth in the Context of COVID-19 in Quebec (Canada)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic has had a major impact on health and social service systems (HSSS) worldwide. It has put tremendous pressure on these systems, threatening access, continuity, and the quality of patient care and services. In Quebec (Canada), the delivery of care and services has radically changed in a short period of time. During the pandemic, telehealth has been widely deployed and used, notwithstanding the decades-long challenges of integrating this service modality into the Quebec HSSS. Adopting a narrative-integrative approach, this article describes and discusses Quebec's experience with the deployment and utilization of telehealth in the context of COVID-19. Firstly, we introduced the achievements and benefits made with the use of telehealth. Secondly, we discussed the challenges and concerns that were revealed or accentuated by the sanitary crisis, such as: (1) training and information; (2) professional and organizational issues; (3) quality of services and patient satisfaction; (4) cost, remuneration, and funding; (5) technology and infrastructure; (6) the emergence of private telehealth platforms in a public HSSS; (7) digital divide and equity; and (8) legal and regulatory issues. Finally, the article presents recommendations to guide future research, policies and actions for a successful integration of telehealth in the Quebec HSSS as well as in jurisdictions and countries facing comparable challenges.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it