A “Not So Quiet” Revolution: Systemic Benefits and Challenges of Telehealth in the Context of COVID-19 in Quebec (Canada)
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".