News Article Portrayal of Virtual Care for Health Care Delivery in the First 7 Months of the COVID-19 Pandemic
Bibliographic record
Abstract
Objectives: The onset of the 2020 coronavirus pandemic resulted in rapid implementation of virtual care solutions at an unprecedented pace. The news media, as a trusted source for many Canadians, plays a vital role during emergencies by reporting on changes in health care protocols, policies, and technologies. This article presents the results of a qualitative analysis of Canadian news articles between February and August of 2020 to identify critical themes with respect to virtual care. Methods: A full-text search of the database Canadian Newsstream resulted in 1542 articles (708 duplicates), of which 294 articles were included in the final analysis. Inductive analysis was used to generate themes and identify voices, contradictions, and tensions in the articles. Results: Analysis generated four themes: coronavirus disease (COVID-19) as a catalyst for virtual care, safety and protection, economic impacts, and telehealth as a model of care. Media portrayals represented some voices (e.g., physicians) while limiting others (e.g., patients), reflected some contradictory messaging with respect to safety and protection, and raised key issues and concerns about virtual health care delivery during the first 7 months of COVID-19. Conclusions: Our findings of successful and rapid uptake, uses and concerns around funding, and privacy and virtual care adoption reported in the news media can be used to inform longer term implementation and sustainability. Policy makers could benefit from crafting messages that balance information and reassurance. Public/patient perspectives, which were largely missing from news media, are needed to gauge receptivity and sustainability.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".