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Record W3184927530 · doi:10.1002/pbc.29251

Exploring illness identity among children and youth living with cancer: A narrative review

2021· review· en· W3184927530 on OpenAlexaff
Stephanie Posa, Fiona J. Moola, Amy C. McPherson, Pia Kontos

Bibliographic record

VenuePediatric Blood & Cancer · 2021
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity Health NetworkToronto Metropolitan UniversityHolland Bloorview Kids Rehabilitation HospitalToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsNormativeIdentity (music)NarrativeMedicineThematic analysisIdentity formationCancerDevelopmental psychologySocial identity approachSocial identity theoryPsychologySelf-conceptSocial psychologyQualitative researchSocial groupSociologyAestheticsPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Children and youth with cancer may find it challenging to integrate illness into their pre-existing identity-a phenomenon known as illness identity. In this critical narrative review, we explored illness identity among children and youth with cancer. METHODS: Three academic databases were searched. Twenty-two articles were included in this review and each underwent thematic analysis. RESULTS: Cancer has both positive and negative influences on the identities of children and youth. Illness identity is expressed creatively through various communicative outlets. Further, external processes such as social support may influence cancer identity. A few studies cited cancer as a hindrance to adult identity development. CONCLUSIONS: Cancer has a profound impact on identity formation among children and youth. Many normative assumptions about time, identity, and child and youth development underlie the existing literature. Future researchers may adopt a critical lens to be inclusive of diverse identity experiences among children and youth with cancer.

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)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.078
GPT teacher head0.353
Teacher spread0.275 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2021
Admission routes1
Has abstractyes

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