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Record W1979174866 · doi:10.1089/153056204773644526

Quebec Population and Telehealth: A Survey on Knowledge and Perceptions

2004· article· en· W1979174866 on OpenAlexaffabout
Marie‐Pierre Gagnon, Alain Cloutier, Jean‐Paul Fortin

Bibliographic record

VenueTelemedicine Journal and e-Health · 2004
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsTelehealthPopulationHealth carePublic healthNursingBusinessTelemedicineMedicinePublic relationsPsychologyPolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.399
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2004
Admission routes2
Has abstractyes

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