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Record W2771017387 · doi:10.1177/0844562117742559

Translation and Validation of the Toronto Pain Management Index, French–Canadian Version

2017· article· en· W2771017387 on OpenAlexaffvenueabout
Dave A. Bergeron, Nicole Bolduc, Cécile Michaud, Johanne Lapré, Patricia Bourgault

Bibliographic record

VenueCanadian Journal of Nursing Research · 2017
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsUniversité du Québec à RimouskiCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsIntraclass correlationPain managementKnowledge translationMedicineIndex (typography)Physical therapyFace validityPsychologyNursingPsychometricsClinical psychologyKnowledge managementComputer science

Abstract

fetched live from OpenAlex

Background To provide effective pain management, nurses must have sufficient knowledge and adequate beliefs about pain management. In Quebec, however, nurses seem to be generally uninvolved in pain management, and there is little significant evidence shedding light on nurses' pain management knowledge and beliefs in postoperative settings. To perform such studies, a valid questionnaire in French to assess nurses' knowledge and beliefs is required. Some valid questionnaires are available in English, but none are available in French. Purpose This article describes the process of translation, adaptation, and preliminary validation of the Toronto Pain Management Index into French. Results For temporal stability of the Toronto Pain Management Index, French-Canadian version, the result of intraclass correlation coefficient for the total score of this questionnaire is 0.59 (CI: 0.44-0.72). Conclusion Following this process, the French version of this questionnaire has suitable face and content validity and can be used to evaluate nurses' knowledge and beliefs about pain management in postoperative settings.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.083
GPT teacher head0.371
Teacher spread0.288 · 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

Citations3
Published2017
Admission routes3
Has abstractyes

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