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Record W3045249650 · doi:10.5935/1415-2762.20200057

BRAZILIAN VERSION OF THE TORONTO PAIN MANAGEMENT INVENTORY - ACUTE CORONARY SYNDROME

2020· article· en· W3045249650 on OpenAlexaboutno aff
Andressa Cristina de Souza Felício, Renata Eloah de Lucena Ferretii-Rebustini, Beatriz Murata Murakami, Filipe Utuari de Andrade Coelho, Camila Takáo Lopes, Sheila O’Keefe-McCarthy, Eduarda Ribeiro dos Santos

Bibliographic record

VenueReme Revista Mineira de Enfermagem · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsAcute coronary syndromeAcute painPain managementMedicineInventory managementEmergency medicinePhysical therapyAnesthesiaInternal medicineOperations managementMyocardial infarctionEngineering

Abstract

fetched live from OpenAlex

Objective: to perform the cross-cultural adaptation of the Toronto Pain Management Inventory - Acute Coronary Syndrome instrument into Brazilian Portuguese and test face validity evidence of the adapted instrument. Methods:we followed the procedures proposed by the Guideline for Establishing Cultural Equivalency of Instruments (RDC/TMD) Consortium Network (phase 1) for cross-cultural adaptation. To measure agreement between the judges in the equivalences analysis, we used the content validity index (CVI). Face validity was performed with nurses during the pre-test and consisted of assessing the easeof understanding when answering the items. Results: the adapted instrument achieved linguistic equivalence. The semantic, idiomatic, experimental and conceptual equivalences had a mean CVI of 98.5 (95% CI 97.1-100.0), 97.8 (95% CI 96.0-99.5), 94.1 (95 % 91.6-96.6) and 99.6 (95% CI 98.9-100.0). In the pre-test, 92.5% of nurses considered the instrument easy to understand and 85% found no difficulty. Conclusion: the adapted instrument is culturally equivalent to the original instrument and shows evidence of face validity. The psychometric properties of the instrument are yet to be investigated.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.273
Teacher spread0.253 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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