Social acceptance and population confidence in telehealth in Quebec
Bibliographic record
Abstract
BACKGROUND: Access to healthcare in remote areas is difficult and telehealth could be a promising avenue if accepted by the population. The aim of this study is to assess social acceptance and population confidence in telehealth in the Province of Quebec. METHODS: We conducted a survey using a questionnaire assessing the social acceptance of and confidence level in telehealth. Two strategies were used: 1) paper questionnaires were sent to two hospitals in Quebec; and 2) online questionnaires were randomly sent by a firm specialized in online survey to a representative sample of the population of the Province of Quebec. Respondents were all residents of the Province of Quebec and 18 years and older. Questions were scored with a four-level Likert scale. RESULTS: A total of 1816 questionnaires were analyzed (229 written and 1,587 online questionnaires). The socio-demographic variables in our samples, especially the online questionnaires, were fairly representative of Quebec's population. Overall, social acceptance scored at 77.71% and confidence level at 65.76%. Both scores were higher in the case of treatment (3 scenarios were proposed) vs. diagnosis (p < 0.05). No difference was found when respondents were asked to respond for themselves and for a member of their family, which demonstrates a true interest in telehealth in Quebec. In addition, we found a significant difference (p < 0.05) between written and online questionnaires regarding social acceptance (80.75% vs. 77.33%) and confidence level (74.84% vs. 64.55%). These differences may be due to social desirability or avidity bias in the written questionnaires. CONCLUSIONS: Our results suggest that the population in Quebec encourages the development of telehealth for real time diagnosis and long distance treatment for regions deprived of healthcare professionals.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".