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Record W4396515149 · doi:10.5737/23688076342196

Enhancing Sibling Support in Oncology: Collaborative Care for Families Facing Cancer in Young People

2024· article· en· W4396515149 on OpenAlexaffvenue
Charlotte Gélinas-Gagné, Miranda D’Amico

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsConcordia University
Fundersnot available
KeywordsSiblingPediatric oncologyCancerChildhood cancerOncologyMedicinePsychologyInternal medicineDevelopmental psychology

Abstract

fetched live from OpenAlex

Purpose: This study investigates the support systems and needs of siblings of young people with cancer, shedding light on the emotional and informational challenges siblings face. This topic area has had relatively little attention. Design and methods: Qualitative interviews were conducted, and thematic analysis was employed to gain in-depth insights into the experiences and perspectives of siblings. While the study's relatively small sample size and participant homogeneity are acknowledged limitations, the approach offers several strengths, including relevance and participant diversity across age cohorts. Results: The findings underscore the essential role of healthcare professionals, particularly nurses, in providing emotional and informational support to siblings. Family-centred care, psychosocial support, tailored interventions, and ongoing research and education are recommended to address the unique needs of siblings effectively. Conclusion: Overall, this study highlights the importance of recognizing and addressing the support needs of siblings in pediatric oncology care, emphasizing their role as a vital component of the family system and advocating for holistic support throughout the cancer journey and beyond.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.384
Teacher spread0.361 · 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.

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

Citations2
Published2024
Admission routes2
Has abstractyes

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