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Record W4225011218 · doi:10.1177/00302228221086057

Loneliness, Social Support Level, Quality of Life and Symptom Management Among Turkish Oncology Patients

2022· article· en· W4225011218 on OpenAlexaboutno aff
Tuğçe Çamlıca, Zeliha Koç

Bibliographic record

VenueOMEGA - Journal of Death and Dying · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessTurkishQuality of life (healthcare)UCLA Loneliness ScaleSocial supportWorryFeelingScale (ratio)Clinical psychologyPsychologyMedicineInternal medicineGerontologyAnxietyPsychiatryNursingPsychotherapist

Abstract

fetched live from OpenAlex

This cross-sectional and correlational study was performed in order to determine the relationships between the perceived loneliness and social support levels of Turkish oncology patients, as well as their quality of life and symptom management. A total of 370 oncology inpatients participated in this study. Data were collected using, the FACT-G Quality of Life Scale, the Multi-Dimensional Scale of Perceived Social Support (MSPSS), the UCLA-Loneliness Scale (UCLA-LS), and the Edmonton Symptom Assessment Scale. A negative advanced significant relationship was found between the MSPSS total scores ( r = −0.754, p < 0.01) and the UCLA-LS total scores. As the social support levels of the patients increased, their loneliness levels were seen to decrease and their quality of life was seen to increase. The patients were found to experience the symptoms of fatigue, worry, and feeling unwell more often as their loneliness levels increased and social support levels decreased.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.324
Teacher spread0.272 · 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 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

Citations9
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueOMEGA - Journal of Death and DyingSame topicCancer survivorship and careFrench-language works237,207