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Record W4411857144 · doi:10.1111/cch.70109

Preparing Implementation of Transition Readiness Screening: What Do Paediatric Cancer Survivors and Caregivers Want?

2025· article· en· W4411857144 on OpenAlexaff
Marika Monarque, Laurianne Buron, Nadège Gendron Granger, Wendy Louis‐Delsoin, Nathalie Labonté, Carole Provost, Zeev Rosberger, Argerie Tsimicalis, Élodie Bergeron, Marco Bonanno, Serge Sultan, Caroline Laverdière, Leandra Desjardins

Bibliographic record

VenueChild Care Health and Development · 2025
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsMcGill UniversityJewish General HospitalUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsFocus groupThematic analysisPreparednessDistressHealth careMedicinePsychologyTransition (genetics)NursingQualitative researchFamily medicineClinical psychologyPolitical scienceBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: Supporting transition from paediatric to adult healthcare is essential for the long-term well-being of paediatric cancer survivors. Guided by an implementation science approach, our overall programme of research seeks to integrate transition readiness screening in the routine care of paediatric cancer survivors, using the validated Transition Readiness Assessment Questionnaire (TRAQ). To plan for the screening implementation, the primary objective of this study was to first assess paediatric cancer survivors' current experiences with transition preparation and their preferences for screening and resources. A secondary focus group with parents was also conducted to complement youth perspectives. METHODS: Focus groups were conducted with 14- to 18-year-old paediatric cancer survivors (n = 13) and parents (n = 6). Focus groups explored perceptions of care at the long-term follow-up clinic, transition preparedness, preferences for TRAQ administration (e.g., format, moment and location) and preferences for transition readiness resources. Interviews were analysed with an inductive thematic analysis approach. RESULTS: This study identified several barriers to TRAQ implementation, including adolescents' lack of transition awareness, attachment to paediatric care and parental distress. Unclear TRAQ items were also noted as a challenge for adolescents. Strategies to address these barriers include regular discussions with healthcare providers, a comprehensive transition resource website, addressing emotional needs, providing parent guidance and offering flexible TRAQ administration options, for example, by leveraging technology (QR codes, choice of online or paper administration). CONCLUSION: This study highlighted the importance of addressing the informational and emotional needs of adolescents and parents for implementation, notably by engaging in discussions with clinicians and tailoring online transition readiness resources. Preferences and suggestions for TRAQ administration and resources will be integrated to align with patients and parents' needs and optimize implementation. KEY MESSAGES: The study identified key barriers to TRAQ implementation, including paediatric cancer survivors' lack of awareness of transition and understanding of the TRAQ, attachment to paediatric care and parental distress about long-term follow-up. Participants suggested strategies such as offering the TRAQ online, integrating it into wait times and enhancing awareness through individualized discussions. Addressing emotional needs through dedicated resources and incorporating discussions about transition-related distress into the TRAQ implementation process is crucial. Participants preferred online resources over paper formats, and a webpage on transition resources was acceptable to both adolescents and parents. Results of this study are an essential first step in the preparation of a successful TRAQ implementation, with findings allowing better planning and adaptation of implementation to the local context and to adolescents and parents.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.027
GPT teacher head0.396
Teacher spread0.368 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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