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Record W4380090323 · doi:10.3390/curroncol30060422

Young Adults’ Lived Experiences with Cancer-Related Cognitive Impairment: An Exploratory Qualitative Study

2023· article· en· W4380090323 on OpenAlexaffvenue
Sitara Sharma, Jennifer Brunet

Bibliographic record

VenueCurrent Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsInstitut du Savoir MontfortOttawa HospitalMontfort HospitalUniversity of Ottawa
Fundersnot available
KeywordsCognitionYoung adultQualitative researchDistressMedicineExploratory researchThematic analysisPsychologyClinical psychologyCoping (psychology)GerontologyPsychiatry

Abstract

fetched live from OpenAlex

Cancer-related cognitive impairment (CRCI; e.g., disrupted memory, executive functioning, and information processing) affects many young adults, causing significant distress, reducing quality of life (QoL), and thwarting their ability to engage in professional, recreational, and social experiences. The purpose of this exploratory qualitative study was to investigate young adults’ lived experiences with CRCI, and any strategies (including physical activity) they use to self-manage this burdensome side effect. Sixteen young adults (Mage = 30.8 ± 6.0 years; 87.5% female; Myears since diagnosis = 3.2 ± 3) who reported clinically meaningful CRCI whilst completing an online survey were interviewed virtually. Four themes comprising 13 sub-themes were identified through an inductive thematic analysis: (1) descriptions and interpretations of the CRCI phenomenon, (2) effects of CRCI on day-to-day and QoL, (3) cognitive–behavioural self-management strategies, and (4) recommendations for improving care. Findings suggest CRCI is detrimental to young adults’ QoL and must be addressed more systematically in practice. Results also illuminate the promise of PA in coping with CRCI, but research is needed to confirm this association, test how and why this may occur, and determine optimal PA prescriptions for young adults to self-manage their CRCI.

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 score0.021
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
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.129
GPT teacher head0.464
Teacher spread0.334 · 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

Citations17
Published2023
Admission routes2
Has abstractyes

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