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Record W2340914121 · doi:10.11622/smedj.2016083

Quality of life of family caregivers of cancer patients in Singapore and globally

2016· article· en· W2340914121 on OpenAlexaboutno aff
HA Lim, JY Tan, Joelle Y.H. Chua, RK Yoong, SE Lim, Ee Heok Kua, Rathi Mahendran

Bibliographic record

VenueSingapore Medical Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Quality (philosophy)Family medicineGerontologyCancerFamily caregiversNursingInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Family caregivers of cancer patients often suffer from impaired quality of life (QOL) due to stress arising from the responsibility of caregiving. Most research on such QOL impairments was conducted in Western populations. Thus, this exploratory study sought to (a) examine the QOL levels of family caregivers of cancer patients in an Asian population in Singapore, in relation to caregivers from other countries within and outside of Asia; and (b) investigate the association between sociodemographic factors and QOL impairments in family caregivers in Singapore. METHODS: A total of 258 family caregivers of cancer patients who were receiving outpatient treatment completed the Caregiver Quality of Life Index-Cancer (CQOLC) and a sociodemographic survey. We compared the published CQOLC total scores from Turkey, Iran, Taiwan, South Korea, the United Kingdom, the United States and Canada with the Singapore dataset and examined the demographic relationships. RESULTS: Caregivers in Singapore and Asia had lower CQOLC total scores than their Western counterparts. Caregivers who were male, of Chinese ethnicity, had parental relationships with their care recipient, or cared for advanced-stage cancer patients were found to have impaired QOL. CONCLUSION: The findings of this study highlight possible areas in which support can be provided for family caregivers of cancer patients, and underscore the need to reconcile cultural diversity, values, societal expectations and demographic characteristics in Singapore.

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)0.001
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 score0.101
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.325
Teacher spread0.299 · 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

Citations111
Published2016
Admission routes1
Has abstractyes

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