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Record W3022298111 · doi:10.4037/ajcc2020763

Translation into Spanish and Cultural Adaptation of the Critical-Care Pain Observation Tool

2020· article· en· W3022298111 on OpenAlexaff
Carmen Mabel Arroyo-Novoa, Milagros I. Figueroa-Ramos, Kathleen Puntillo, Céline Gélinas

Bibliographic record

VenueAmerican Journal of Critical Care · 2020
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsJewish General Hospital
FundersNational Institute on Minority Health and Health Disparities
KeywordsAdaptation (eye)Intensive careMedicineIntensive care unitKnowledge translationMEDLINEProcess (computing)NursingPsychologyComputer scienceKnowledge managementIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The Critical-Care Pain Observation Tool (CPOT) is recommended for evaluating pain behaviors in patients in the intensive care unit who are unable to report pain. The source of the only published Spanish version of the CPOT does not verify that it underwent a formal translation process. OBJECTIVE: To describe the translation into Spanish and cultural adaptation of the original French version of the CPOT. METHODS: Key persons in the translation process included one with a master's degree in translation, a critical care physician, nurse faculty members with vast experience in intensive care units, and the instrument's developer. This team followed the Principles of Good Practice for the Translation and Cultural Adaptation Process for Patient-Reported Outcomes Measures as a guide to translate and culturally adapt the CPOT. RESULTS: The first Spanish-language version was back translated to French and was also compared with the English version. Revisions necessitated a second version, which was submitted to experts in critical care. Their modifications required a third version, which was back translated to French and discussed with the CPOT developer, after which a fourth version was created. Finally, a linguistic expert proofread the tool, and the translation leaders incorporated the recommendations, thereby obtaining a final Spanish version. CONCLUSION: The Spanish version is ready to undergo validation with patients in the intensive care unit, which is the next step toward its use in assessing pain behaviors among patients in intensive care units where Spanish is spoken.

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)0.000
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.043
GPT teacher head0.325
Teacher spread0.282 · 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 designQualitative
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

Citations2
Published2020
Admission routes1
Has abstractyes

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