MétaCan
Menu
Back to cohort
Record W25270129 · doi:10.3171/2014.7.focus14364

A model for telemedicine adoption : a survey of physicians in the provinces of Quebec and Nova Scotia

2000· dissertation· en· W25270129 on OpenAlexaboutno aff
Dragos Vieru

Bibliographic record

VenueNeurosurgical FOCUS · 2000
Typedissertation
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsTelemedicineNova scotiaStructural equation modelingTechnology acceptance modelSample (material)PerceptionPsychologyTest (biology)Affect (linguistics)UsabilityApplied psychologyFamily medicineMedical educationMedicineComputer scienceStatisticsGeographyHealth careMathematicsPolitical science

Abstract

fetched live from OpenAlex

The primary goal of this study is to evaluate and predict factors that could affect physicians' adoption behavior of Telemedicine technology. Based on the theoretical foundations of some of the most recognized technology acceptance models in the academic world, a model is proposed and then empirically tested. A sample of 390 physicians that were, at the time of the survey, either already using or about to use Telemedicine technology were selected to test a number of hypotheses. The Partial Least Square (PLS) method of structural equation modeling was used to assess the relationships between perceptions and behavioral intention to adopt Telemedicine. The outcomes of the study revealed that Perceived Usefulness has a strong and positive impact on physicians' adoption behavior and that Perceived Ease of Use is significantly related to Perceived Usefulness.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.100
GPT teacher head0.379
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueNeurosurgical FOCUSSame topicTechnology Adoption and User BehaviourFrench-language works237,207