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Record W2937932439 · doi:10.1111/ecc.13048

Uncertainty and sense‐of‐self as targets for intervention for cancer‐related fatigue

2019· article· en· W2937932439 on OpenAlexaff
Naomi Dolgoy, Mei Krishnasamy, Margaret L. McNeely

Bibliographic record

VenueEuropean Journal of Cancer Care · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineCancer-related fatigueIntervention (counseling)Psychological interventionPerspective (graphical)Health professionalsCancerSet (abstract data type)Health careClinical psychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Cancer-related fatigue (CRF) can be a devastating consequence of cancer and cancer treatments, negatively impacting 50%-90% of cancer patients regardless of age, sex or diagnosis. Limited evidence and research exist to inform effective patient-centred interventions. To target symptom management, there must first be a broader understanding of the symptoms and the lived experience of the persons experiencing CRF and those caring for them, from a supportive as well as a healthcare perspective. This study set out to consider whether components of the language used or descriptors reported by patients, family members, and/or healthcare professionals may provide new insights for potential targets for intervention development. Descriptors from 84 responses (n = 84) from cancer survivors, family members and healthcare professionals were analysed for content. The descriptors reiterate the physical, emotional and functional consequences of CRF, but also reflect two new potential targets for intervention to mitigate the impacts of CRF: uncertainty and sense-of-self.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.528

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 head0.016
GPT teacher head0.309
Teacher spread0.292 · 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 designNot applicable
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

Citations10
Published2019
Admission routes1
Has abstractyes

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