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Record W2468501021 · doi:10.2196/mhealth.5715

Redesign and Validation of Sisom, an Interactive Assessment and Communication Tool for Children With Cancer

2016· article· en· W2468501021 on OpenAlexvenueno aff
Susann Arvidsson, Britt‐Mari Gilljam, Jens Nygren, Cornelia M. Ruland, Trude Nordby-Bøe, Petra Svedberg

Bibliographic record

VenueJMIR mhealth and uhealth · 2016
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsNorwegianAcronymConversationPsychologymHealthHealth careMedical educationMedicinePsychological interventionPsychiatryCommunication

Abstract

fetched live from OpenAlex

BACKGROUND: Children with cancer undergo intensive and long treatment periods that expose them and their families to a number of difficult physical, mental, and social challenges. Empowering children by actively involving them in their care can help them to cope with these challenges. It can, however, be difficult for children to be involved and talk about their illness experiences in a "traditional" conversation with health care professionals, especially for younger children. Sisom (Norwegian acronym "Si det som det er" or "Tell it how it is") is an interactive computer-based assessment and communication tool to give children (aged 6-12 years) with cancer a "voice" in their care. Because of technological advances and widespread use of mobile devices Sisom had to be redesigned to better meet the needs of children of today. OBJECTIVE: To redesign Sisom for use on mobile devices and to validate and adapt it for use in a Swedish population of children with cancer. METHODS: A user-experience design was used. Content adaptation included forward-backward translation by Swedish and Norwegian translators. Healthy children (n=5), children with experiences of cancer treatment (n=5) and their parents (n=5), and pediatric nurses (n=2) were then involved in culturally adapting Sisom to the Swedish context. The iterative low- and high-fidelity evaluation was supported by a think aloud method, semistructured interviews, and drawings to capture children's views of Sisom. The redesign and evaluation continued until no further changes or improvements were identified by the participants or the researchers. RESULTS: Children, parents, and pediatric nurses offered many suggestions for improvements to the original version in terms of content, aesthetics, and usability of Sisom. The most significant change that emerged through user input was a modification that entailed not using problem-focused statements in the assessment items. The parents and pediatric nurses considered the revised assessment items to be general and less diagnosis specific. The evaluation of aesthetics resulted in brighter colors and more positive and exciting details in the animations. The evaluation of usability included improvements of the verbal instructions on how to navigate in Sisom 2, and also that the answers to assessment items in Sisom 2 should be saved to provide the children with the option to pause and to continue answering the remaining assessment items at a later stage. CONCLUSIONS: Overall, this paper describes the process of using user-experience design with children in order to redesign and validate an interactive assessment and communication tool and how the outcomes of this process resulted in a new version, Sisom 2. All participants confirmed the usability and qualities of using the final version. Future research should be directed toward the implementation of Sisom 2 in clinical practice and to evaluate outcomes from individual and organizational levels.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.446
Teacher spread0.378 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations54
Published2016
Admission routes1
Has abstractyes

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