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Record W4414193503 · doi:10.2196/preprints.76877

Cancer Care Experiences Among Adolescents, Caregivers, and Health Care Providers in a Regional Canadian Context: Protocol for a Qualitative Study (Preprint)

2025· article· en· W4414193503 on OpenAlexaboutno aff
Joanne Tay, Mohammad Jarrar, Jessica Kichler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosocialQualitative researchThematic analysisHealth careReflexivityFocus groupCancerRelevance (law)MEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND Adolescents diagnosed with cancer face significant disruptions to their development, education, and social lives. These challenges are pronounced in regional settings, where access to specialized, developmentally appropriate oncology care is limited. In Ontario, Canada, youth aged 12 to 18 years often fall between pediatric and adult care systems, leading to fragmented services, unmet psychosocial needs, and long-term disparities in survivorship. While international literature has examined the cancer experiences of adolescents and young adults, most research originates from the United States, limiting its relevance in the Canadian context. In Ontario’s regional hospitals, youth and families face disparities in care quality, specialist access, and logistical challenges. More research is needed to inform equitable, youth-centered cancer care models. OBJECTIVE This study aims to explore the lived experiences of youth cancer survivors, their caregivers, and health care providers (HCPs) in a regional Canadian context. The study investigates four research questions: (1) What are the daily experiences and psychosocial needs of youth during and after treatment? (2) How do caregivers navigate cancer care for youth? (3) What are HCPs’ perspectives on delivering cancer care for youth? (4) What recommendations can youth, caregivers, and HCPs offer to improve cancer care systems for youth? METHODS We applied a qualitative descriptive design using semistructured web-based interviews and reflexive thematic analysis. Participants were recruited through a multimethod strategy, including clinician referral, posters, digital outreach, and professional networks. The anticipated sample includes 24 participants: 8 (33%) youth cancer survivors (aged 12 to 18 years at diagnosis), 8 (33%) caregivers, and 8 (33%) HCPs. Eligibility criteria were defined to ensure safety, diversity, and relevance. Interviews were conducted via Microsoft Teams, transcribed verbatim, and analyzed using the 6-phase reflexive thematic analysis approach described by Braun and Clarke. NVivo software supported coding and theme development. Demographic data were analyzed descriptively to contextualize the findings. RESULTS As of September 2025, 14 participants had completed interviews: 6 (43%) caregivers, 6 (43%) HCPs, and 2 (14%) youths. Youth recruitment has been challenging due to the developmental stage and competing commitments. Data collection concluded in December 2024. Preliminary transcript coding was completed in early 2025, with final analysis and synthesis of themes completed in June 2025. This study was funded in August 2023, and results are expected to be published in Fall 2025 and Winter 2026. CONCLUSIONS This study will provide critical insight into cancer care delivery for youth in a regional Canadian setting. Integrating youth, caregiver, and HCP perspectives will illuminate systemic gaps, relational dynamics, and context-specific barriers. The findings will inform policy, education, and service innovations aimed at improving equity, continuity, and developmental responsiveness in oncology care for adolescents and young adults in Canada. INTERNATIONAL REGISTERED REPORT RR1-10.2196/76877

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)0.032
metaresearch head score (Gemma)0.021
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.515
Threshold uncertainty score0.964

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.021
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.006
Science and technology studies0.0170.005
Scholarly communication0.0060.003
Open science0.0050.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0460.004

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 head0.052
GPT teacher head0.432
Teacher spread0.380 · 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
GenreProtocol

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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Citations0
Published2025
Admission routes1
Has abstractyes

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