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Record W4235109548 · doi:10.2196/preprints.25240

UTAUT2-Based Questionnaire: Translation to Canadian French, Cross-Cultural Adaptation and Cognitive Debriefing (Preprint)

2020· preprint· en· W4235109548 on OpenAlexaboutno aff
Isabelle Pagé, Marianne Roos, Olivier Collin, Sean Lynch, Marie‐Ève Lamontagne, Hugo Massé‐Alarie, Andréanne K. Blanchette

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsDebriefingCLARITYPsychologyCognitionUnified theory of acceptance and use of technologyMedical educationApplied psychologyKnowledge managementComputer scienceSocial psychologyExpectancy theoryMedicine

Abstract

fetched live from OpenAlex

BACKGROUND From the early stages of development of a new tool or device to its implementation in real-life settings, it is crucial to take the perception of potential users into consideration. A number of theories have been proposed to better understand acceptance of technology. The Unified Theory of Acceptance and Use of Technology (UTAUT) combines eight of these theories and has been shown to accurately predict technology acceptance. An extended version, the UTAUT2, was proposed in 2012 and includes three new concepts to accurately analyze acceptance and usage of technology from a consumer perspective. No validated Canadian French version of this tool currently exists. OBJECTIVE The main objective was to cross-culturally adapt the UTAUT2-based questionnaire for use in the French-Canadian population. A cognitive debriefing involving potential users (workers) and experts (rehabilitation clinicians) was included to confirm clarity and relevance of questionnaire content. METHODS The procedure was developed in line with published guidelines and included five steps: (1) Forward translation by two bilingual members of the research team, (2) Synthesis of the translated versions by the research team, (3) Backward translation by two other bilingual members, (4) Synthesis by a multidisciplinary committee and proposal of the Pre-final Canadian French UTAUT2-based questionnaire, and (5) Cognitive debriefing. Cognitive debriefing consisted in the assessment of the clarity of the pre-final version content by a French-Canadian sample of potential responders (i.e. workers) and by an expert panel of rehabilitation professionals. Experts also appraised the relevance of each item of the pre-final version. Any questionnaire content or item not reaching an 80% inter-rater agreement for clarity or relevance was re-evaluated by the multidisciplinary committee until a final version was unanimously approved. RESULTS The multidisciplinary committee (n=6) was composed of researchers and clinicians from four different backgrounds. Twelve workers and 12 experts participated in the cognitive debriefing step. Each content or item (n=40) was judged as "clear" by at least 92% of the worker sample. When clarity was assessed by the experts, six terms/phrases did not reach 80% agreement and were therefore reviewed by the multidisciplinary committee. Four of the 27 items were also reviewed by the committee following the experts’ relevance assessment. The final version of the Canadian French UTAUT2-based questionnaire was approved unanimously by the members of the multidisciplinary committee. CONCLUSIONS The final version of the Canadian French version of the UTAUT2-based questionnaire is culturally appropriate for use in French-speaking Canada. Further studies are necessary to determine its psychometric properties.

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)0.012
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.622

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0380.012

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 head0.184
GPT teacher head0.409
Teacher spread0.226 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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Same topicTechnology Adoption and User BehaviourFrench-language works237,207