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Record W3176575770 · doi:10.1155/2021/5511426

Translation, Cross-Cultural Adaptation, and Psychometric Validation of the Chinese/Mandarin Cardiac Rehabilitation Barriers Scale (CRBS-C/M)

2021· article· en· W3176575770 on OpenAlexaff
Xia Liu, Adeleke Fowokan, Sherry L. Grace, B. Ding, Shu Meng, Xiu Chen, Xia Y, Yaqing Zhang

Bibliographic record

VenueRehabilitation Research and Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsYork UniversityUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsCronbach's alphaMandarin ChineseExploratory factor analysisScale (ratio)Confirmatory factor analysisMedicineClinical psychologyPsychologyPsychometricsGerontologyStatisticsMathematicsCartography

Abstract

fetched live from OpenAlex

Objective. Cardiovascular diseases are among the leading causes of morbidity in China and around the world. Cardiac rehabilitation (CR) effectively mitigates this burden; however, utilization is low. CR barriers in China have not been well characterized; this study sought to translate, cross-culturally adapt, and psychometrically validate the CR Barriers Scale in Chinese/Mandarin (CRBS-C/M). Methods. Independent translations of the 21-item CRBS were conducted by two bilingual health professionals, followed by back-translation. A Delphi process was undertaken with five experts to consider the semantics and cross-cultural relevance of the items. Following finalization, 380 cardiac patients from 11 hospitals in Shanghai were administered a validation survey including the translated CRBS. Following exploratory and confirmatory factor analysis, internal consistency was assessed. Validity was tested through assessing the association of the CRBS-C/M with the CR Information Awareness Questionnaire. Results. Items were refined and finalized. Factor analysis of CRBS-C/M ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mtext>Kaiser</a:mtext> <a:mtext> </a:mtext> <a:mtext>Meyer</a:mtext> <a:mtext> </a:mtext> <a:mtext>Olkin</a:mtext> <a:mo>=</a:mo> <a:mn>0.867</a:mn> </a:math> , Bartlett’s test <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>p</c:mi> <c:mo>&lt;</c:mo> <c:mn>0.001</c:mn> </c:math> ) revealed five factors: perceived CR need, external logistical factors, time conflicts, program and health system-level factors, and comorbidities/lack of vitality; Cronbach’s alpha ( <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>α</e:mi> </e:math> ) of the subscales ranged from 0.67 to 0.82. The mean total CRBS score was significantly lower in patients who participated in CR compared with those who did not, demonstrating criterion validity ( <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:mn>2.35</g:mn> <g:mo>±</g:mo> <g:mn>0.71</g:mn> </g:math> vs. <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" id="M5"> <i:mn>3.08</i:mn> <i:mo>±</i:mo> <i:mn>0.55</i:mn> </i:math> ; <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" id="M6"> <k:mi>p</k:mi> <k:mo>&lt;</k:mo> <k:mn>0.001</k:mn> </k:math> ). Construct validity was supported by the significant associations between total CRBS scores and CR awareness, sex, living situation, city size, income, diagnosis/procedure, disease severity, and several risk factors (all <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" id="M7"> <m:mi>p</m:mi> <m:mo>&lt;</m:mo> <m:mn>0.05</m:mn> </m:math> ). Conclusions. CRBS-C/M is reliable and valid, so barriers can be identified and mitigated in Mandarin-speaking patients.

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)0.005
metaresearch head score (Gemma)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.472
Teacher spread0.411 · 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.

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

Citations20
Published2021
Admission routes1
Has abstractyes

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