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Record W7132958150

A Family's Perception of Childhood Cancer Survivorship: A Case Study in Resilience

2016· dissertation· W7132958150 on OpenAlexaff
Lisa-Marie Bianchi

Bibliographic record

VenueTSpace · 2016
Typedissertation
Language
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsChildhood cancerPsychological resiliencePerceptionQualitative researchFamily lifeAdaptation (eye)Coping (psychology)Cancer
DOInot available

Abstract

fetched live from OpenAlex

Research on families experiencing childhood cancer and life in remission has focused on negative outcomes and variables. Few studies have explored the process of family resilience through this type of adversity. The purpose of this qualitative case study is to describe the experiences of a family who has survived a paediatric cancer. The central question that informed the study is: What are the accounts of members of a family in dealing with childhood cancer and life in survivorship? One family was interviewed over several months and continued to assist in the data analysis and creation of the findings for this study. It was found that cancer subculture and online community support promoted resilience and adaptation for the family as they dealt with the demands and stress of childhood cancer and remission. Recommendations to the Resiliency Model of Family Stress, Adjustment, and Adaptation, healthcare professionals, and further research were discussed.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0170.006
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.430
Teacher spread0.374 · 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 designCase report
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
Published2016
Admission routes1
Has abstractyes

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