Linguistic Validation of an Interactive Communication Tool to Help French-Speaking Children Express Their Cancer Symptoms
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".