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Record W2158299212 · doi:10.1177/1043454214543021

Usability Testing of an Online Self-Management Program for Adolescents With Cancer

2014· article· en· W2158299212 on OpenAlexafffund
Jennifer Stinson, Abha A. Gupta, France Dupuis, Bruce Dick, Caroline Laverdière, Sylvie LeMay, Lillian Sung, Elizabeth Dettmer, Stephanie Gomer, Janie Lober, Carol Chan

Bibliographic record

VenueJournal of Pediatric Oncology Nursing · 2014
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversity of AlbertaUniversité de MontréalHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsUsabilityThe InternetSample (material)Medical educationPsychologyQualitative researchComputer scienceWorld Wide WebApplied psychologyMedicineHuman–computer interaction

Abstract

fetched live from OpenAlex

The objective of this study was to explore the usability of a bilingual (English and French) Internet-based self-management program for adolescents with cancer and their parents and refine the Internet program. A qualitative study design with semistructured, audio-taped interviews and observation was undertaken with 4 iterative cycles. A purposive sample of English-speaking and French-speaking adolescents with cancer and one of their parents/caregivers was recruited. Adolescents and parents provided similar feedback on how to improve the usability of the Internet program. Most changes to the website were completed after the initial cycles of English and French testing. Both groups also found information presented on the website to be appropriate, credible, and relevant to their experiences of going through cancer. Participants reported the program would have been extremely helpful when they were first diagnosed with cancer. Usability testing uncovered some issues that affected the usability of the website that led to refinements in the online program.

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)0.002
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 score0.464
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.044
GPT teacher head0.393
Teacher spread0.349 · 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

Citations61
Published2014
Admission routes2
Has abstractyes

Explore more

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