Quebec Population and Telehealth: A Survey on Knowledge and Perceptions
Bibliographic record
Abstract
Telehealth is widely considered to be a promising tool that addresses many of the challenges currently facing the health care system in Canada. However, diffusion of telehealth will ultimately depend on its acceptance among health care professionals and the general population. This study explores public understanding and perceptions of telehealth in the Province of Quebec (Canada). A telephone survey involving interviews with a random sample of 1242 individuals was conducted in various Quebec regions. Only 8.9% of respondents were familiar with the term "telehealth," whereas telehealth applications, especially teletriage, appeared to be more commonly known. A large majority of respondents believed telehealth could facilitate access to health care services, improve quality of care, and reduce health care expenditures. Legal responsibility in cases of medical error was reported as the leading public concern related to telehealth. Furthermore, nearly 50% of Quebec's population would use telehealth services when offered to them. The principal factors weighing in favor of willingness to use telehealth services were: knowledge of telehealth applications perception of telehealth benefits, reduced barriers to telehealth, and the fact of being female. Promoting the use of telehealth in the general population and dissipating concerns related to its applications will require global educational strategy that will inform the public about the benefits of telehealth as well as addressing ethical and legal issues.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".