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Experience of Fatigue in Adolescents Living With Cancer

2006· article· en· W2033523320 on OpenAlexaff
Emma Ream, Faith Gibson, Jacqueline Edwards, Beth Seption, Anne Mulhall, Alison Richardson

Bibliographic record

VenueCancer Nursing · 2006
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsGibson Energy (Canada)
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Exploratory researchPerceptionClinical psychologyQualitative researchPhysical therapyPsychologyNursing

Abstract

fetched live from OpenAlex

This article reports on a small-scale exploratory study conducted with cohorts of adolescents during and after treatment of cancer to explore experiences of fatigue and perceptions of its impact on functioning. A concurrent mixed method design was used to enable detailed understanding of the phenomenon of fatigue in these groups of individuals through convergence of quantitative and qualitative data. Participants completed an investigator-designed Fatigue and Quality of Life Diary for a period of 1 week. Second, they took part in a semistructured interview to explore issues around fatigue and functioning in more detail. Eight adolescents undergoing treatment participated in the study, along with 6 in early remission (1-2 years off treatment) and 8 receiving follow-up (5 or more years off treatment). Data gained from these sources suggested that fatigue can be a considerable problem for adolescents during and after treatment, and that it may not necessarily abate quickly. Some individuals perceived that their quality of life remained compromised many years after treatment, and it seemed that fatigue might play an important part in this. These preliminary findings suggest that research into management of fatigue in this adolescent group is warranted, along with research and development to determine how best to provide supportive care once treatment finishes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.358
Teacher spread0.322 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations57
Published2006
Admission routes1
Has abstractyes

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