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Record W2981676914 · doi:10.5173/ceju.2017.923

Basic Issues Concerning Health-Related Quality of Life

2017· article· en· W2981676914 on OpenAlexaboutno aff
Roman Sosnowski, Marta Kulpa, Urszula Ziętalewicz, Jan Wolski, Robert Nowakowski, Robert Bakuła, Tomasz Demkow

Bibliographic record

VenueEditor-in-Chief s Voice List of Authors is an Important Element in a Scientific Publication · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsQuality of life (healthcare)Conceptual modelPsychologyQuality (philosophy)Relation (database)Health related quality of lifeGerontologyApplied psychologyMedicinePsychotherapistComputer scienceEpistemologyData miningDatabaseDiseasePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Chronic diseases such as cancer have a strong influence on both physical health and quality of life, which together comprise the concept of health-related quality of life (HRQoL) - in other words, the complete state of physical, social, and psychological functioning. Herein, we review the literature on the theory of HRQoL in relation to oncological diseases. MATERIAL AND METHODS: A literature search of English-language publications that included an analysis of the conceptual models of HRQoL was performed using PubMed. The data were screened and synthesized by all authors and relevant papers were selected. RESULTS: We outline the theoretical models most often used to conceptualize HRQoL, including the Centre for Health Promotion model from the University of Toronto, the conceptual model of Wilson and Cleary and the contextual model of Ashing-Giwa formulated specifically for cancer patients. CONCLUSIONS: Understanding the theoretical basis of HRQoL is indispensable for valid research in this area.

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)0.016
metaresearch head score (Gemma)0.051
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: Editorial · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.051
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.005
Scholarly communication0.0060.006
Open science0.0020.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0130.002

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 head0.061
GPT teacher head0.367
Teacher spread0.305 · 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
GenreEditorial

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

Citations116
Published2017
Admission routes1
Has abstractyes

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