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Record W4412203995 · doi:10.2196/60897

Developing and Integrating Digital Sources in an Accessible and Sustainable Online Platform for Adolescents and Young Adult Cancer Survivors: Collaborative Design Approach

2025· article· en· W4412203995 on OpenAlexvenueno aff
Carla Vlooswijk, Sophia H. E. Sleeman, Jonas Pluis, Daphne Bakker, Lisanne de Groot, Eveliene Manten‐Horst, Peter Heine, Mies van Eenbergen, Pieter Vandekerckhove

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisFocus groupStakeholderStakeholder engagementSustainabilityKnowledge managementPsychologyMedicineMedical educationComputer scienceQualitative researchPublic relationsBusinessPolitical scienceMarketingSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Digital support for adolescent and young adult cancer survivors is fragmented and results in lacking a reliable overview of support services available to them. Collaborative design promises to integrate perspectives of diverse stakeholders and could help to develop an online platform, which increases access and has a long-term perspective. However, it has not yet been explored how collaborative design can be used more strategically to develop an online platform with these aims. OBJECTIVE: This study aimed to explore how a collaborative design approach could be applied to develop an online platform for adolescent and young adult cancer survivors, focusing on three key objectives: integrating existing resources, improving accessibility, and ensuring long-term financial sustainability. METHODS: In this action research study, we reflect on a collaborative design process to develop an online platform for adolescent and young adult cancer survivors. A quantitative questionnaire was sent out to adolescent and young adult cancer survivors and health care professionals. Stakeholders were actively engaged in stakeholder consensus meetings, and project management was carried out through monthly design meetings to facilitate coordination and decision-making of the development of the platform. Afterwards, a focus group was conducted among the project group to evaluate the collaborative design approach, analyzed using an inductive thematic approach. RESULTS: Through the collaborative design approach, several Dutch organizations collaborated to develop, enhance, and combine online services for adolescent and young adult cancer survivors and their relatives. A dedicated online "young and cancer" platform for adolescent and young adult cancer survivors was developed, which integrates different types of information tools and supportive interactive elements from different sources. The integration of different resources into one platform improves the access and user experience of adolescent and young adult cancer survivors when it comes to online support. Through the reflection about the collaborative process, three themes were identified: (1) value of stakeholder participation; (2) conditions for working with adolescents and young adults with lived experience; and (3) collaboration between adolescents and young adults with lived experience and professionals from different backgrounds and organizations. CONCLUSIONS: A collaborative design approach can be used to efficiently develop an online platform for adolescent and young adult cancer survivors. The collaboration among professionals, including online developers, researchers, and adolescents and young adults with lived experience facilitated a direct translation of insights into the platform, while the support of the national cancer platform provides long-term sustainability. This study highlights the importance of strategic stakeholder selection and the intense involvement of stakeholders through a collaborative design approach.

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)0.044
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0060.006
Scholarly communication0.0090.006
Open science0.0030.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.082
GPT teacher head0.438
Teacher spread0.356 · 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 designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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