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Record W2884989435 · doi:10.1097/jcn.0000000000000505

Validation of a Spanish Version of the Information Needs in Cardiac Rehabilitation Scale to Assess Information Needs and Preferences in Cardiac Rehabilitation

2018· article· en· W2884989435 on OpenAlexaff
Gabriela Lima de Melo Ghisi, Claudia V. Anchique, Rosalía Fernández, Daniel Quesada-Chaves, Marina Gordillo, Sheiles Acosta, Julia Fernandez, Blanca Arrieta-Loaiciga, Marco Heredia, Paul Oh

Bibliographic record

VenueThe Journal of Cardiovascular Nursing · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsCytodiagnostics (Canada)Toronto Rehabilitation Institute
Fundersnot available
KeywordsCronbach's alphaExploratory factor analysisRehabilitationMedicineReliability (semiconductor)Scale (ratio)PsychometricsPhysical therapyInternal consistencyClinical psychology

Abstract

fetched live from OpenAlex

BACKGROUND: The identification of information needs is considered the first step to increase knowledge that ultimately could improve health outcomes in cardiac rehabilitation (CR). OBJECTIVE: The aim of this study was to psychometrically validate the Spanish Information Needs in Cardiac Rehabilitation (INCR). METHODS: The Spanish INCR was psychometrically tested in 184 patients undergoing CR. The internal consistency was assessed using Cronbach α, factor structure was assessed using exploratory factor analysis, and criterion validity regarding educational level, occupation, and duration in CR was assessed. RESULTS: Cronbach α was .97. Factor analysis revealed 10 factors, all internally consistent. Criterion validity was supported by significant differences in total INCR scores by educational level (P < .01), occupation (P < .01), and duration in CR (P < .05). Emergency/safety was the greatest information need perceived by patients. CONCLUSIONS: The Spanish INCR was demonstrated to have good reliability and validity. This tool can be applicable in clinical and research settings, assessing patients' information needs during CR and as part of education programming.

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)0.007
metaresearch head score (Gemma)0.001
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 score0.635
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.012
GPT teacher head0.288
Teacher spread0.276 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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