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Record W4307358601 · doi:10.1177/21582440221130040

How Does Social and Work Life Change for Fathers of Children With Cancer?

2022· article· en· W4307358601 on OpenAlexaff
Jaehee Yi, Min Ah Kim, Jina Sang, Kwynn M. Gonzalez‐Pons

Bibliographic record

VenueSAGE Open · 2022
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsThematic analysisPediatric cancerChildhood cancerDevelopmental psychologyPsychologyCancerWork (physics)Social psychologyQualitative researchMedicineSociologySocial science

Abstract

fetched live from OpenAlex

Caring for a child with cancer greatly affects fathers’ social lives, with fathers experiencing conflicts between work demands and their desire to be with their sick child. To date, fathers’ unique experiences of caring for a child with cancer remain poorly understood. This study aimed to understand the impact of a child’s cancer diagnosis on the social and work relationships of their father through in-depth interviews with 20 Korean fathers of a child with cancer within 5 years of the diagnosis. Thematic analysis yielded five themes related to how a child’s cancer diagnosis affected the father’s relationships at work and in social situations: (a) shifting priorities, (b) changes in work, (c) support and struggles at work, (d) not being social out of guilt, and (e) pent-up stress. Overall, the findings highlight that fathers experienced conflicting roles and constraints in social relationships after their child’s cancer diagnosis. This should be considered when developing and implementing social services for families with children diagnosed 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)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0020.002
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 head0.064
GPT teacher head0.335
Teacher spread0.271 · 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
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

Citations1
Published2022
Admission routes1
Has abstractyes

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