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Record W2406762436 · doi:10.1177/1043454216646532

Linguistic Validation of an Interactive Communication Tool to Help French-Speaking Children Express Their Cancer Symptoms

2016· article· en· W2406762436 on OpenAlexaff
Argerie Tsimicalis, Sylvie Le May, Jennifer Stinson, Janet E. Rennick, Marie‐France Vachon, Julie Louli, Sarah Bérubé, Stephanie Treherne, Sunmoo Yoon, Trude Nordby Bøe, Cornelia M. Ruland

Bibliographic record

VenueJournal of Pediatric Oncology Nursing · 2016
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsMontreal Children's HospitalHospital for Sick ChildrenUniversity of TorontoUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineMcGill UniversityShriners Hospitals for Children - Canada
Fundersnot available
KeywordsUsabilityComprehensionPsychologyHealth careHealth professionalsKnowledge translationProcess (computing)Test (biology)Medical educationQualitative researchApplied psychologyLinguisticsMedicineComputer scienceKnowledge managementHuman–computer interactionSociology

Abstract

fetched live from OpenAlex

Sisom is an interactive tool designed to help children communicate their cancer symptoms. Important design issues relevant to other cancer populations remain unexplored. This single-site, descriptive, qualitative study was conducted to linguistically validate Sisom with a group of French-speaking children with cancer, their parents, and health care professionals. The linguistic validation process included 6 steps: (1) forward translation, (2) backward translation, (3) patient testing, (4) production of a Sisom French version, (5) patient testing this version, and (6) production of the final Sisom French prototype. Five health care professionals and 10 children and their parents participated in the study. Health care professionals oversaw the translation process providing clinically meaningful suggestions. Two rounds of patient testing, which included parental participation, resulted in the following themes: (1) comprehension, (2) suggestions for improving the translations, (3) usability, (4) parental engagement, and (5) overall impression. Overall, Sisom was well received by participants who were forthcoming with input and suggestions for improving the French translations. Our proposed methodology may be replicated for the linguistic validation of other e-health tools.

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)0.001
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 score0.628
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.026
GPT teacher head0.374
Teacher spread0.348 · 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

Citations19
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of Pediatric Oncology NursingSame topicChildhood Cancer Survivors' Quality of LifeFrench-language works237,207