MétaCan
Menu
Back to cohort
Record W4394240925 · doi:10.6084/m9.figshare.19196744

UTAUT2-based questionnaire: cross-cultural adaptation to Canadian French

2022· dataset· en· W4394240925 on OpenAlexaboutno aff
Isabelle Pagé, Marianne Roos, Olivier Collin, Sean Lynch, Marie‐Ève Lamontagne, Hugo Massé‐Alarie, Andréanne K. Blanchette

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptation (eye)PsychologyGeographyNeuroscience

Abstract

fetched live from OpenAlex

The extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT2) aims to better understand acceptance of technology. The objective of this study was to translate the English UTAUT2-based questionnaire to Canadian French. The translation included five steps: (1) Forward translation, (2) Synthesis of the translated versions, (3) Backward translation, (4) Synthesis by a multidisciplinary committee and proposal of the Pre-final Canadian French version, and (5) Cognitive debriefing. Cognitive debriefing included the assessment of the questionnaire items’ clarity by (1) a sample of workers, and (2) rehabilitation professionals. Any item not reaching an 80% inter-rater agreement for clarity or relevance was re-evaluated. The multidisciplinary committee included six researchers and clinicians from four different backgrounds. Twelve workers and 12 experts participated in the cognitive debriefing. Each item (n = 40) was judged as “clear” by at least 92% of the worker sample. Six and four items were reviewed following clarity and relevance assessments. The final version was approved unanimously. A Canadian French version of the UTAUT2-based questionnaire has been developed. Studies are necessary to examine cultural and semantic equivalence of the original and translated versions, and the cultural appropriateness of the questionnaire.IMPLICATIONS FOR REHABILITATIONThere is an exponential growth in technology, including in the rehabilitation field.Implementing rehabilitation technology into clinical practice remains a challenge.The UTAUT model, and its extension, help to better understand the acceptance of technology before its implementation.The UTAUT2-based questionnaire evaluates the acceptability of rehabilitation technology prior to implementation. There is an exponential growth in technology, including in the rehabilitation field. Implementing rehabilitation technology into clinical practice remains a challenge. The UTAUT model, and its extension, help to better understand the acceptance of technology before its implementation. The UTAUT2-based questionnaire evaluates the acceptability of rehabilitation technology prior to implementation.

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)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.369
Threshold uncertainty score0.742

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.030
GPT teacher head0.313
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueFigshareSame topicNatural Language Processing TechniquesFrench-language works237,207